Statistical downscaling of projecting rainfall amount based on SVC-RVM model
<p>The objective of this study is to evaluate and compare the proposed statistical</p><p>downscaling model in Kelantan and Terengganu states. The study also investigates</p><p>the most accurate imputation methods in handling the m...
Saved in:
| Main Author: | |
|---|---|
| Format: | thesis |
| Language: | eng |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://ir.upsi.edu.my/detailsg.php?det=8788 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
oai:ir.upsi.edu.my:8788 |
|---|---|
| record_format |
uketd_dc |
| institution |
Universiti Pendidikan Sultan Idris |
| collection |
UPSI Digital Repository |
| language |
eng |
| topic |
QA Mathematics |
| spellingShingle |
QA Mathematics Nurul Ainina Filza Sulaiman Statistical downscaling of projecting rainfall amount based on SVC-RVM model |
| description |
<p>The objective of this study is to evaluate and compare the proposed statistical</p><p>downscaling model in Kelantan and Terengganu states. The study also investigates</p><p>the most accurate imputation methods in handling the missing atmospheric data and</p><p>the important predictors for a statistical downscaling method by reducing the</p><p>dimensionality data. The data used in this study include atmospheric data (predictors)</p><p>and daily rainfall data (predictand) from 1998 until 2007. As part of its methodology,</p><p>this study had used an imputation method for handling missing data. Then, Principal</p><p>Component Analysis (PCA) was applied to rectify the issue of high-dimensional data</p><p>and select predictors for a two-phase model. The two-phase machine learning</p><p>techniques were introduced as a precise statistical downscaling method in Kelantan and</p><p>Terengganu states. The first phase is a classification using the Support Vector</p><p>Classification (SVC) that determines dry and wet days. Subsequently, a regression</p><p>estimates the amount of rainfall based on the frequency of wet days using the Support</p><p>Vector Regression (SVR), Artificial Neural Network (ANN), and Relevant Vector</p><p>Machine (RVM). The proposed model was analysed by using the performance</p><p>measures that are Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency</p><p>(NSE). The result of imputation methods shows Random Forest (RF) is having the</p><p>lowest RMSE value and the highest NSE value. The analysis of PCA results indicates</p><p>two selected Principal Components cut-off eigenvalues at 1.6 and 70.29% cumulative</p><p>percentage of the total variance. In the conclusion of this study, the comparison of</p><p>results from the SVC and RVM hybridizations reveals that the hybrid reproduces the</p><p>most reasonable daily rainfall projection and supports the high rainfall extremes,</p><p>making it a perfect candidate for rainfall prediction research. The implication of this</p><p>study is to establish the relationship between predictand variables and predictors in</p><p>order to improve predicting accuracy in climate change projections by using a</p><p>hybridization model.</p> |
| format |
thesis |
| qualification_name |
|
| qualification_level |
Master's degree |
| author |
Nurul Ainina Filza Sulaiman |
| author_facet |
Nurul Ainina Filza Sulaiman |
| author_sort |
Nurul Ainina Filza Sulaiman |
| title |
Statistical downscaling of projecting rainfall amount based on SVC-RVM model |
| title_short |
Statistical downscaling of projecting rainfall amount based on SVC-RVM model |
| title_full |
Statistical downscaling of projecting rainfall amount based on SVC-RVM model |
| title_fullStr |
Statistical downscaling of projecting rainfall amount based on SVC-RVM model |
| title_full_unstemmed |
Statistical downscaling of projecting rainfall amount based on SVC-RVM model |
| title_sort |
statistical downscaling of projecting rainfall amount based on svc-rvm model |
| granting_institution |
Universiti Pendidikan Sultan Idris |
| granting_department |
Fakulti Sains dan Matematik |
| publishDate |
2022 |
| url |
https://ir.upsi.edu.my/detailsg.php?det=8788 |
| _version_ |
1776104566550429696 |
| spelling |
oai:ir.upsi.edu.my:87882023-03-17 Statistical downscaling of projecting rainfall amount based on SVC-RVM model 2022 Nurul Ainina Filza Sulaiman QA Mathematics <p>The objective of this study is to evaluate and compare the proposed statistical</p><p>downscaling model in Kelantan and Terengganu states. The study also investigates</p><p>the most accurate imputation methods in handling the missing atmospheric data and</p><p>the important predictors for a statistical downscaling method by reducing the</p><p>dimensionality data. The data used in this study include atmospheric data (predictors)</p><p>and daily rainfall data (predictand) from 1998 until 2007. As part of its methodology,</p><p>this study had used an imputation method for handling missing data. Then, Principal</p><p>Component Analysis (PCA) was applied to rectify the issue of high-dimensional data</p><p>and select predictors for a two-phase model. The two-phase machine learning</p><p>techniques were introduced as a precise statistical downscaling method in Kelantan and</p><p>Terengganu states. The first phase is a classification using the Support Vector</p><p>Classification (SVC) that determines dry and wet days. Subsequently, a regression</p><p>estimates the amount of rainfall based on the frequency of wet days using the Support</p><p>Vector Regression (SVR), Artificial Neural Network (ANN), and Relevant Vector</p><p>Machine (RVM). The proposed model was analysed by using the performance</p><p>measures that are Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency</p><p>(NSE). The result of imputation methods shows Random Forest (RF) is having the</p><p>lowest RMSE value and the highest NSE value. The analysis of PCA results indicates</p><p>two selected Principal Components cut-off eigenvalues at 1.6 and 70.29% cumulative</p><p>percentage of the total variance. In the conclusion of this study, the comparison of</p><p>results from the SVC and RVM hybridizations reveals that the hybrid reproduces the</p><p>most reasonable daily rainfall projection and supports the high rainfall extremes,</p><p>making it a perfect candidate for rainfall prediction research. The implication of this</p><p>study is to establish the relationship between predictand variables and predictors in</p><p>order to improve predicting accuracy in climate change projections by using a</p><p>hybridization model.</p> 2022 thesis https://ir.upsi.edu.my/detailsg.php?det=8788 https://ir.upsi.edu.my/detailsg.php?det=8788 text eng closedAccess Masters Universiti Pendidikan Sultan Idris Fakulti Sains dan Matematik <p>Abbott, D. (1999). Combining models to improve classifier accuracy and robustness.</p><p>Proceedings of Second International Conference on , January 1999, 17.</p><p></p><p>Abdel-Kader, H., Salam, M. A.-E., & ... (2021). Hybrid Machine Learning Model for</p><p>Rainfall Forecasting. Journal of Intelligent , 1(1), 512.</p><p>https://doi.org/10.5281/zenodo.3376685</p><p></p><p>Acua, E., & Rodriguez, C. (2004). The Treatment of Missing Values and its Effect</p><p>on Classifier Accuracy. Classification, Clustering, and Data Mining</p><p>Applications. https://doi.org/10.1007/978-3-642-17103-1_60</p><p></p><p>Advani, V. (2021). What is Machine Learning? How Machine Learning Works and</p><p>future of it? Great Learning. https://www.mygreatlearning.com/blog/what-ismachine-</p><p>learning/</p><p></p><p>Agrawal, A. (2019). Highlights the advantages and disadvantages of machine</p><p>learning. Cyber Infrastructure, CIS. https://www.cisin.com/coffeebreak/</p><p>Enterprise/highlights-the-advantages-and-disadvantages-of-machinelearning.</p><p>html</p><p></p><p>Ahmadkhani, S., & Adibi, P. (2016). Face recognition using supervised probabilistic</p><p>principal component analysis mixture model in dimensionality reduction without</p><p>loss framework. IET Computer Vision, 10(3), 193201.</p><p>https://doi.org/10.1049/iet-cvi.2014.0434</p><p></p><p>Aksornsingchai, P., & Srinilta, C. (2011). Statistical downscaling for rainfall and</p><p>temperature prediction in Thailand. IMECS 2011 - International</p><p>MultiConference of Engineers and Computer Scientists 2011, 1(January 1948),</p><p>356361.</p><p></p><p>Albon, C. (2017). SVC Parameters When Using RBF Kernel. GitHub.</p><p>https://chrisalbon.com/machine_learning/support_vector_machines/svc_paramet</p><p>ers_using_rbf_kernel/</p><p></p><p>Ali, A. H., & Abdullah, M. Z. (2020). An efficient model for data classification based</p><p>on SVM grid parameter optimization and PSO feature weight selection.</p><p>International Journal of Integrated Engineering, 12(1), 112.</p><p>https://doi.org/10.30880/ijie.2020.12.01.001</p><p></p><p>Aljuaid, T., & Sasi, S. (2017). Proper imputation techniques for missing values in data</p><p>sets. Proceedings of the 2016 International Conference on Data Science and</p><p>Engineering, ICDSE 2016. https://doi.org/10.1109/ICDSE.2016.7823957</p><p></p><p>Alsaber, A. R., Pan, J., & Al-Hurban, A. (2021). Handling complex missing data</p><p>using random forest approach for an air quality monitoring dataset: A case study</p><p>of kuwait environmental data (2012 to 2018). International Journal of</p><p>Environmental Research and Public Health, 18(3), 126.</p><p>https://doi.org/10.3390/ijerph18031333</p><p></p><p>Amirabadizadeh, M., Ghazali, A. H., Huang, Y. F., & Wayayok, A. (2016).</p><p>Downscaling daily precipitation and temperatures over the Langat River Basin in</p><p>Malaysia : A comparison of two statistical downscaling approaches.</p><p>International Journal of Water Resources and Environmental Engineering,</p><p>8(December), 120136. https://doi.org/10.5897/IJWREE2016.0585</p><p></p><p>Anandhi, A., Srinivas, V. V., NAnjundiah, R. S., & Kumar, D. N. (2008).</p><p>Downscaling precipitation to river basin in India for IPCC SRES scenarions</p><p>using support vector machine. International Journal of Climatology, 28(March</p><p>2008), 401420. https://doi.org/10.1002/joc</p><p></p><p>Andridge, R. R., & Little, R. J. A. (2010). A review of hot deck imputation for survey</p><p>non-response. International Statistical Review, 78(1), 4064.</p><p>https://doi.org/10.1111/j.1751-5823.2010.00103.x</p><p></p><p>Angra, S., & Ahuja, S. (2017). Machine learning and its applications: A review.</p><p>Proceedings of the 2017 International Conference On Big Data Analytics and</p><p>Computational Intelligence, ICBDACI 2017, April 2020, 5760.</p><p>https://doi.org/10.1109/ICBDACI.2017.8070809</p><p></p><p>Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., & Ridella, S. (2012). The K in Kfold</p><p>Cross Validation. European Symposium on Artificial Neural Networks-</p><p>ESANN 2012 Proceedings, April.</p><p></p><p>Anguita, D., Ghio, A., Ridella, S., & Sterpi, D. (2009). K-Fold Cross Validation for</p><p>Error Rate Estimate in Support Vector Machines. Vessels Fuel Consumption</p><p>Forecast and Trim Optimisation: a Data Analytics Perspective View project KFold</p><p>Cross Validation for Error Rate Estimate in Support Vector Machines.</p><p>Proc. DMIN Int. Conf. Data Mining, January.</p><p>https://www.researchgate.net/publication/220704948</p><p></p><p>Anguita, D., Ridella, S., Rivieccio, F., & Zunino, R. (2003). Hyperparameter design</p><p>criteria for support vector classifiers. Neurocomputing, 55(12), 109134.</p><p>https://doi.org/10.1016/S0925-2312(03)00430-2</p><p></p><p>Arifin, F., Robbani, H., Annisa, T., & MaArof, N. N. M. I. (2019). Variations in the</p><p>Number of Layers and the Number of Neurons in Artificial Neural Networks:</p><p>Case Study of Pattern Recognition. Journal of Physics: Conference Series,</p><p>1413(1). https://doi.org/10.1088/1742-6596/1413/1/012016</p><p></p><p>ASCE. (2000). Artificial Neural Network in Hydrology I: Preliminary Concepts. In</p><p>Journal of Hydrologic Engineering (Vol. 5, Issue 2).</p><p></p><p>Assent, I. (2012). Clustering high dimensional data. Wiley Interdisciplinary Reviews:</p><p>Data Mining and Knowledge Discovery, 2(4), 340350.</p><p>https://doi.org/10.1002/widm.1062</p><p></p><p>Ayesha, S., Hanif, M. K., & Talib, R. (2020). Overview and comparative study of</p><p>dimensionality reduction techniques for high dimensional data. Information</p><p>Fusion, 59(May 2019), 4458. https://doi.org/10.1016/j.inffus.2020.01.005</p><p></p><p>Azid, A., Juahir, H., Toriman, M. E., Kamarudin, M. K. A., Saudi, A. S. M., Hasnam,</p><p>C. N. C., Aziz, N. A. A., Azaman, F., Latif, M. T., Zainuddin, S. F. M., Osman,</p><p>M. R., & Yamin, M. (2014). Prediction of the level of air pollution using</p><p>principal component analysis and artificial neural network techniques: A case</p><p>study in Malaysia. Water, Air, and Soil Pollution, 225(8).</p><p>https://doi.org/10.1007/s11270-014-2063-1</p><p></p><p>Baghanam, A. H., Eslahi, M., Sheikhbabaei, A., & Seifi, A. J. (2020). Assessing the</p><p>impact of climate change over the northwest of Iran: an overview of statistical</p><p>downscaling methods. Theoretical and Applied Climatology, 141(34), 1135</p><p>1150. https://doi.org/10.1007/s00704-020-03271-8</p><p></p><p>Bahari, N. I. S., Ahmad, A., & Aboobaider, B. M. (2014). Application of support</p><p>vector machine for classification of multispectral data. IOP Conference Series:</p><p>Earth and Environmental Science, 20(1). https://doi.org/10.1088/1755-</p><p>1315/20/1/012038</p><p></p><p>Bala, R., & Kumar, D. (2017). Classification Using ANN: A Review. International</p><p>Journal of Computational Intelligence Research, 13(7), 18111820.</p><p>http://www.ripublication.com</p><p></p><p>Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data</p><p>analyses. Journal of School Psychology, 48(1), 537.</p><p>https://doi.org/10.1016/j.jsp.2009.10.001</p><p></p><p>Barnard, J., & Meng, X.-L. (1999). Applications of multiple imputation in medical</p><p>studies: from AIDS to NHANES. Statistical Methods in Medical Research, 8(1),</p><p>1736. https://doi.org/10.1177/096228029900800103</p><p></p><p>Batista, G., & Monard, M. (2002). A Study of K -Nearest Neighbour as an Imputation</p><p>Method. Argentine Symposium on Artificial Intelligence, October.</p><p></p><p>Beaudoin, A., Bernier, P. Y., Guindon, L., Villemaire, P., Guo, X. J., Stinson, G.,</p><p>Bergeron, T., Magnussen, S., & Hall, R. J. (2014). Mapping attributes of</p><p>Canadas forests at moderate resolution through kNN and MODIS imagery.</p><p>Canadian Journal of Forest Research, 44(5), 521532.</p><p>https://doi.org/10.1139/cjfr-2013-0401</p><p></p><p>Bell, W., Brockwell, P. J., & Davis, R. A. (2009). Time Series: Theory and Methods.</p><p>In Journal of the American Statistical Association (Vol. 84, Issue 405).</p><p>https://doi.org/10.2307/2289896</p><p></p><p>Benestad, R., & Benestad, R. (2016). Downscaling Climate Information. In Oxford</p><p>Research Encyclopedia of Climate Science (Issue June).</p><p>https://doi.org/10.1093/acrefore/9780190228620.013.27</p><p></p><p>Bengio, Y., & Grandvalet, Y. (2004). No Unbiased Estimator of the Variance of KFold</p><p>Cross Validation. Journal of Machine Learning Research, 5, 10891105.</p><p>https://doi.org/10.1016/S0006-291X(03)00224-9</p><p></p><p>Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and</p><p>New Zealand Journal of Public Health, 25(5), 464469.</p><p>https://doi.org/10.1111/j.1467-842X.2001.tb00294.x</p><p></p><p>Beretta, L., & Santaniello, A. (2016). Nearest neighbor imputation algorithms: A</p><p>critical evaluation. BMC Medical Informatics and Decision Making, 16(74).</p><p>https://doi.org/10.1186/s12911-016-0318-z</p><p></p><p>Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of crossvalidation</p><p>for evaluating autoregressive time series prediction. Computational</p><p>Statistics and Data Analysis, 120, 7083.</p><p>https://doi.org/10.1016/j.csda.2017.11.003</p><p></p><p>Berrar, D. (2018). Cross-validation. Encyclopedia of Bioinformatics and</p><p>Computational Biology: ABC of Bioinformatics, 13(April), 542545.</p><p>https://doi.org/10.1016/B978-0-12-809633-8.20349-X</p><p></p><p>Berzofsky, M., Biemer, P., & Kalsbeek, W. (2008). A Brief History of Classification</p><p>Error Models. Proceeding of Joint Statistical Modeetings, 36673673.</p><p></p><p>Bethere, L., Sennikovs, J., & Bethers, U. (2017). Climate indices for the Baltic states</p><p>from principal component analysis. Earth System Dynamics, 8(4), 951962.</p><p>https://doi.org/10.5194/esd-8-951-2017</p><p></p><p>Bhattacharya, A. (2014). Curse of Dimensionality. Fundamentals of Database</p><p>Indexing and Searching, 141148. https://doi.org/10.1201/b17767-13</p><p></p><p>Bhattacharya, D., Nisha, M. G., & Pillai, G. N. (2015). Relevance vector-machinebased</p><p>solar cell model. International Journal of Sustainable Energy, 34(10),</p><p>685692. https://doi.org/10.1080/14786451.2014.885030</p><p></p><p>Bhavsar, H., & Ganatra, A. (2012). A Comparative Study of Training Algorithms for</p><p>Supervised Machine Learning. International Journal of Soft Computing and</p><p>Engineering, 2(4), 7481.</p><p></p><p>Bing, Q., Gong, B., Yang, Z., Shang, Q., & Zhou, X. (2015). Short-Term Traffic Flow</p><p>Local Prediction Based on Combined Kernel Function Relevance Vector</p><p>Machine Model. Mathematical Problems in Engineering, 2015.</p><p>https://doi.org/10.1155/2015/154703</p><p></p><p>Bhner, J., & Bechtel, B. (2017). GIS in Climatology and Meteorology. In</p><p>Comprehensive Geographic Information Systems (Vol. 3).</p><p>https://doi.org/10.1016/B978-0-12-409548-9.09633-0</p><p></p><p>Boisberranger, J. du, Bossche, J. Van den, & Estve, L. (2017). RBF SVM</p><p>parameters. Scikit-Learn Developers. https://scikitlearn.</p><p>org/stable/about.html#authors</p><p></p><p>Borra, S., & Di Ciaccio, A. (2010). Measuring the prediction error. A comparison of</p><p>cross-validation, bootstrap and covariance penalty methods. Computational</p><p>Statistics and Data Analysis, 54(12), 29762989.</p><p>https://doi.org/10.1016/j.csda.2010.03.004</p><p></p><p>Breiman, L. (2001). Random Forests. Machine Language, 45(1), 532.</p><p>https://doi.org/10.14569/ijacsa.2016.070603</p><p></p><p>Breiman, L., Cutler, A., Liaw, A., & Wiener, M. (2018). Package randomForest.</p><p>CRAN. https://doi.org/10.1023/A</p><p></p><p>Brence, J. R., & Brown, D. E. (2006). Improving the Robust Random Forest</p><p>Regression Algorithm. In Systems and Information Engineering Technical</p><p>Papers, Department of Systems and Information Engineering.</p><p></p><p>Brownlee, J. (2020). Train-Test Split for Evaluating Machine Learning Algorithms.</p><p>Python Machine Learning. https://machinelearningmastery.com/train-test-splitfor-</p><p>evaluating-machine-learning-algorithms/</p><p></p><p>Bunkley, Ni. (2008). Joseph Juran, 103, Pioneer in Quality Control, Dies. The New</p><p>York Times. https://www.nytimes.com/2008/03/03/business/03juran.html</p><p></p><p>Brger, G. (1996). Expanded downscaling for generating local weather scenarios.</p><p>Climate Research, 7(2), 111128. https://doi.org/10.3354/cr007111</p><p></p><p>Burman, P. (1989). A comparative study of ordinary cross-validation, v-fold crossvalidation</p><p>and the repeated learning-testing methods. Biometrika, 76(3), 503</p><p>514. https://doi.org/10.1093/biomet/76.3.503</p><p></p><p>Campion, W. M., & Rubin, D. B. (1989). Multiple Imputation for Nonresponse in</p><p>Surveys. In Journal of Marketing Research (Vol. 26, Issue 4).</p><p>https://doi.org/10.2307/3172772</p><p></p><p>Carleo, G., Cirac, I., Cranmer, K., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto,</p><p>L., & Zdeborov, L. (2019). Machine learning and the physical sciences.</p><p>Reviews of Modern Physics, 91(4), 45002.</p><p>https://doi.org/10.1103/RevModPhys.91.045002</p><p></p><p>Castellano, C. M., & DeGaetano, A. T. (2017). Downscaling extreme precipitation</p><p>from CMIP5 simulations using historical analogs. Journal of Applied</p><p>Meteorology and Climatology, 56(9), 24212439.</p><p>https://doi.org/10.1175/JAMC-D-16-0250.1</p><p></p><p>Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute</p><p>error (MAE)? -Arguments against avoiding RMSE in the literature. Geoscientific</p><p>Model Development, 7(3), 12471250. https://doi.org/10.5194/gmd-7-1247-2014</p><p></p><p>Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods.</p><p>Computers and Electrical Engineering, 40(1), 1628.</p><p>https://doi.org/10.1016/j.compeleceng.2013.11.024</p><p></p><p>Che Mat Nor, S. M., Shaharudin, S. M., Ismail, S., Zainuddin, N. H., & Tan, M. L.</p><p>(2020). A comparative study of different imputation methods for daily rainfall</p><p>data in east-coast Peninsular Malaysia. Bulletin of Electrical Engineering and</p><p>Informatics, 9(2), 635643. https://doi.org/10.11591/eei.v9i2.2090</p><p></p><p>Cheema, J. R. (2014). A Review of Missing Data Handling Methods in Education</p><p>Research. Review of Educational Research, 84(4), 487508.</p><p>https://doi.org/10.3102/0034654314532697</p><p></p><p>Chen, C., & Shyu, M. L. (2011). Clustering-based binary-class classification for</p><p>imbalanced data sets. Proceedings of the 2011 IEEE International Conference on</p><p>Information Reuse and Integration, IRI 2011, 384389.</p><p>https://doi.org/10.1109/IRI.2011.6009578</p><p></p><p>Chen, S., Gu, C., Lin, C., Zhang, K., & Zhu, Y. (2020). Multi-kernel optimized</p><p>relevance vector machine for probabilistic prediction of concrete dam</p><p>displacement. Engineering with Computers, 0123456789.</p><p>https://doi.org/10.1007/s00366-019-00924-9</p><p></p><p>Chen, S. H., Jain, L., & Tai, C. C. (2005). Computational economics: A perspective</p><p>from computational intelligence. In Computational Intelligence and its</p><p>Applications Series (Issue May 2016). Idea Group Publishing.</p><p>https://doi.org/10.4018/978-1-59140-649-5</p><p></p><p>Chen, S. T., Yu, P. S., & Tang, Y. H. (2010). Statistical downscaling of daily</p><p>precipitation using support vector machines and multivariate analysis. Journal of</p><p>Hydrology, 385(14), 1322. https://doi.org/10.1016/j.jhydrol.2010.01.021</p><p></p><p>Chen, Z. (2001). Data-Mining and Uncertain Reasoning: An Integrated Approach. In</p><p>Information Visualization. Wiley, New York.</p><p>https://doi.org/10.1057/palgrave.ivs.9500041</p><p></p><p>Cheng, C. H., & Yang, J. H. (2016). A novel rainfall forecast model based on the</p><p>integrated non-linear attribute selection method and support vector regression.</p><p>Journal of Intelligent and Fuzzy Systems, 31(2), 915925.</p><p>https://doi.org/10.3233/JIFS-169021</p><p></p><p>Cheng, C. T., Niu, W. J., Feng, Z. K., Shen, J. J., & Chau, K. W. (2015). Daily</p><p>reservoir runoff forecasting method using artificial neural network based on</p><p>quantum-behaved particle swarm optimization. Water (Switzerland), 7(8), 4232</p><p>4246. https://doi.org/10.3390/w7084232</p><p></p><p>Chhabra, G., Vashisht, V., & Ranjan, J. (2017). A Comparison of Multiple Imputation</p><p>Methods for Data with Missing Values. Indian Journal of Science and</p><p>Technology, 10(19), 17. https://doi.org/10.17485/ijst/2017/v10i19/110646</p><p></p><p>Chhabra, G., Vashisht, V., & Ranjan, J. (2019). A review on missing data value</p><p>estimation using imputation algorithm. Journal of Advanced Research in</p><p>Dynamical and Control Systems, 11(7 Special Issue), 312318.</p><p></p><p>Cho, M. Y., & Hoang, T. T. (2017). Feature Selection and Parameters Optimization of</p><p>SVM Using Particle Swarm Optimization for Fault Classification in Power</p><p>Distribution Systems. Computational Intelligence and Neuroscience, 19.</p><p>https://doi.org/10.1155/2017/4135465</p><p></p><p>Coulibaly, P. (2004). Downscaling daily extreme temperatures with genetic</p><p>programming. Geophysical Research Letters, 31(16), 14.</p><p>https://doi.org/10.1029/2004GL020075</p><p></p><p>Crusoveanu, L. (2021). Epoch in Neural Networks. Baeldung.</p><p>https://www.baeldung.com/cs/epoch-neural-networks</p><p></p><p>Cummins, N., Sethu, V., Epps, J., & Krajewski, J. (2015). Relevance Vector Machine</p><p>for Depression Prediction Industrial Psychology , Rhenish University of Applied</p><p>Sciences Cologne , Germany. Interspeech 2015, 1(2), 110114.</p><p></p><p>Daniel, F. (2020). What is Machine Learning? Emerj The Al Research and Advisory</p><p>Company. https://emerj.com/ai-glossary-terms/what-is-machine-learning/</p><p></p><p>Das, J., & Nanduri, U. V. (2018). Assessment and evaluation of potential climate</p><p>change impact on monsoon flows using machine learning technique over</p><p>Wainganga River basin, India. Hydrological Sciences Journal, 63(7), 1020</p><p>1046. https://doi.org/10.1080/02626667.2018.1469757</p><p></p><p>Davey, A., & Savla, J. (2010). Statistical Power Analysis with Missing Data.</p><p>Routledge Taylor & Francis Group, LLC.</p><p></p><p>Dawson, C. W., Abrahart, R. J., Shamseldin, A. Y., & Wilby, R. L. (2006). Flood</p><p>estimation at ungauged sites using artificial neural networks. Journal of</p><p>Hydrology, 319(14), 391409. https://doi.org/10.1016/j.jhydrol.2005.07.032</p><p></p><p>Deo, R. C., Samui, P., & Kim, D. (2016). Estimation of monthly evaporative loss</p><p>using relevance vector machine, extreme learning machine and multivariate</p><p>adaptive regression spline models. Stochastic Environmental Research and Risk</p><p>Assessment, 30(6), 17691784. https://doi.org/10.1007/s00477-015-1153-y</p><p></p><p>Department of Irrigation and Drainage. (2018). Hydrological Standard for Rainfall</p><p>Station Instrumentation.</p><p></p><p>Desai, K. M., Survase, S. A., Saudagar, P. S., Lele, S. S., & Singhal, R. S. (2008).</p><p>Comparison of artificial neural network (ANN) and response surface</p><p>methodology (RSM) in fermentation media optimization: Case study of</p><p>fermentative production of scleroglucan. Biochemical Engineering Journal,</p><p>41(3), 266273. https://doi.org/10.1016/j.bej.2008.05.009</p><p></p><p>Devak, M., & Dhanya, C. T. (2014). Downscaling of Precipitation in Mahanadi Basin</p><p>, India. International Journal of Civil Engineering Research, 5(2), 111120.</p><p></p><p>Dhiraj, K. (2019). Top 4 advantages and disadvantages of Support Vector Machine or</p><p>SVM. Medium. https://dhirajkumarblog.medium.com/top-4-advantages-anddisadvantages-</p><p>of-support-vector-machine-or-svm-a3c06a2b107</p><p></p><p>Dhurandhar, A., & Dobra, A. (2009). Evaluating Evaluation Measure. In Proceedings</p><p>of Evaluation Methods in Machine Learning Workshop in International</p><p>Conference on Machine Learning (ICML) 2009. https://doi.org/10.1002/pdh.264</p><p></p><p>Dominick, D., Juahir, H., Latif, M. T., Zain, S. M., & Aris, A. Z. (2012). Spatial</p><p>assessment of air quality patterns in Malaysia using multivariate analysis.</p><p>Atmospheric Environment, 60, 172181.</p><p>https://doi.org/10.1016/j.atmosenv.2012.06.021</p><p></p><p>Dong, Y., Wang, J., Wang, C., & Guo, Z. (2017). Research & application of hybrid</p><p>forecasting model based on an optimal feature selection system-A case study on</p><p>electrical load forecasting. Energies, 10(4). https://doi.org/10.3390/en10040490</p><p></p><p>Dorado, J., RabuAL, J. R., Pazos, A., Rivero, D., Santos, A., & Puertas, J. (2003).</p><p>Prediction and modeling of the rainfall-runoff transformation of a typical urban</p><p>basin using ann and gp. Applied Artificial Intelligence, 17(4), 329343.</p><p>https://doi.org/10.1080/713827142</p><p></p><p>Drago, C., & Scepi, G. (2015). Time series clustering from high dimensional data. In</p><p>Lecture Notes in Computer Science (including subseries Lecture Notes in</p><p>Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7627, Issue</p><p>December 2014). https://doi.org/10.1007/978-3-662-48577-4_5</p><p></p><p>Duhan, D., & Pandey, A. (2015). Statistical downscaling of temperature using three</p><p>techniques in the Tons River basin in Central India. Theoretical and Applied</p><p>Climatology, 121(34), 605622. https://doi.org/10.1007/s00704-014-1253-5</p><p></p><p>Efron, B., & Gong, G. (1985). A leisurely look at the Bootstrap, the Jackknife and</p><p>Cross-Validation. American Statistician, 37(1), 3648.</p><p></p><p>El-Shafie, A., Mukhlisin, M., Najah, A. A., & Taha, M. R. (2011). Performance of</p><p>artificial neural network and regression techniques for rainfall-runoff prediction.</p><p>International Journal of Physical Sciences, 6(8), 19972003.</p><p>https://doi.org/10.5897/IJPS11.314</p><p></p><p>Enders, C. K. (2010). Applied missing data analysis. Guilford Press.</p><p>https://books.google.com/books?hl=en&lr=&id=MN8ruJd2tvgC&oi=fnd&pg=P</p><p>A1&dq=Enders,+2010&ots=dJnDs_Vls8&sig=gEP41sXuZcAE2DlqF1qEOo9A</p><p>H8Q</p><p></p><p>Engel, D., Httenberger, L., & Hamann, B. (2012). A survey of dimension reduction</p><p>methods for high-dimensional data analysis and visualization. OpenAccess Series</p><p>in Informatics, 27, 135149. https://doi.org/10.4230/OASIcs.VLUDS.2011.135</p><p></p><p>Erdal, H. I., & Karakurt, O. (2013). Advancing monthly streamflow prediction</p><p>accuracy of CART models using ensemble learning paradigms. Journal of</p><p>Hydrology, 477, 119128. https://doi.org/10.1016/j.jhydrol.2012.11.015</p><p></p><p>Erichson, N. B., Zheng, P., Manohar, K., Brunton, S. L., Kutz, J. N., & Aravkin, A.</p><p>Y. (2020). Sparse Principal Component Analysis via Variable Projection. SIAM</p><p>Journal on Applied Mathematics, 80(2), 9771002.</p><p></p><p>Falkenberg Nielsen, O., & Johnsen, G. (2015). Normal aldring. Anatomi Og</p><p>Fysiologi, 1. Alsvg H. Omsorg-med udgangspunkt i Kari Mart.</p><p></p><p>Fang, C., & Wang, C. (2020). Time Series Data Imputation: A Survey on Deep</p><p>Learning Approaches. http://arxiv.org/abs/2011.11347</p><p></p><p>Ferguson, K. (2018). Why Its Important to Standardize Your Data. Human of Data</p><p>by Atlan. https://humansofdata.atlan.com/2018/12/datastandardization/#:~:</p><p>text=Standardized data is essential for,data to measure it</p><p>against.</p><p></p><p>Fogarty, D. J. (2006). Multiple imputation as a missing data approach to reject</p><p>inference on consumer credit scoring. Interstat, December 2000, 141.</p><p>http://interstat.statjournals.net/YEAR/2006/articles/0609001.pdf</p><p></p><p>Forghani, Y., Tabrizi, R. S., Yazdi, H. S., & Akbarzadeh-T, M. R. (2011). Fuzzy</p><p>support vector regression. 2011 1st International EConference on Computer and</p><p>Knowledge Engineering, ICCKE 2011, Vc, 2833.</p><p>https://doi.org/10.1109/ICCKE.2011.6413319</p><p></p><p>Fushiki, T. (2011). Estimation of prediction error by using K-fold cross-validation.</p><p>Statistics and Computing, 21(2), 137146. https://doi.org/10.1007/s11222-009-</p><p>9153-8</p><p></p><p>Gaag, M. van der, Hoffman, T., Remijsen, M., Hijman, R., de Haan, L., van Meijel,</p><p>B., van Harten, P. N., Valmaggia, L., de Hert, M., Cuijpers, A., & Wiersma, D.</p><p>(2006). The five-factor model of the Positive and Negative Syndrome Scale II: A</p><p>ten-fold cross-validation of a revised model. Schizophrenia Research, 85(13),</p><p>280287. https://doi.org/10.1016/j.schres.2006.03.021</p><p></p><p>Gao, L., Song, J., Liu, X., Shao, J., Liu, J., & Shao, J. (2017). Learning in highdimensional</p><p>multimedia data: the state of the art. Multimedia Systems, 23(3),</p><p>303313. https://doi.org/10.1007/s00530-015-0494-1</p><p></p><p>Gao, Y., Merz, C., Lischeid, G., & Schneider, M. (2018). A review on missing</p><p>hydrological data processing. Environmental Earth Sciences, 77(2), 47.</p><p>https://doi.org/10.1007/s12665-018-7228-6</p><p></p><p>Gaur, A., & Simonovic, S. P. (2018). Introduction to physical scaling: A model aimed</p><p>to bridge the gap between statistical and dynamic downscaling approaches. In</p><p>Trends and Changes in Hydroclimatic Variables: Links to Climate Variability</p><p>and Change. Elsevier Inc. https://doi.org/10.1016/B978-0-12-810985-4.00004-9</p><p></p><p>Geisser, S. (1975). The predictive sample reuse method with applications. Journal of</p><p>the American Statistical Association, 70(350), 320328.</p><p>https://doi.org/10.1080/01621459.1975.10479865</p><p></p><p>Ghahramani, Z. (2004). Unsupervised Learning. Machine Learning, 72112.</p><p></p><p>Ghasemi, F., Mehridehnavi, A., Prez-Garrido, A., & Prez-Snchez, H. (2018).</p><p>Neural network and deep-learning algorithms used in QSAR studies: merits and</p><p>drawbacks. Drug Discovery Today, 23(10), 17841790.</p><p>https://doi.org/10.1016/j.drudis.2018.06.016</p><p></p><p>Ghosh, S., & Mujumdar, P. P. (2008). Statistical downscaling of GCM simulations to</p><p>streamflow using relevance vector machine. Advances in Water Resources,</p><p>31(1), 132146. https://doi.org/10.1016/j.advwatres.2007.07.005</p><p></p><p>Ghritlahre, H. K., & Prasad, R. K. (2018). Application of ANN technique to predict</p><p>the performance of solar collector systems - A review. Renewable and</p><p>Sustainable Energy Reviews, 84(September 2017), 7588.</p><p>https://doi.org/10.1016/j.rser.2018.01.001</p><p></p><p>Gill, M. K., Asefa, T., Kaheil, Y., & McKee, M. (2007). Effect of missing data on</p><p>performance of learning algorithms for hydrologic predictions: Implications to</p><p>an imputation technique. Water Resources Research, 43(7), 112.</p><p>https://doi.org/10.1029/2006WR005298</p><p></p><p>Golub, G. H., Heath, M., & Wahba, G. (1979). Generalized Cross-Validation as a</p><p>Method for Choosing a Good Ridge Parameter. Technometrics, 21(2), 215223.</p><p></p><p>Goly, A., Teegavarapu, R. S. V., & Mondal, A. (2014). Development and evaluation</p><p>of statistical downscaling models for monthly precipitation. Earth Interactions,</p><p>18(18), 128. https://doi.org/10.1175/EI-D-14-0024.1</p><p></p><p>Gondara, L. (2016). Random forest with random projection to impute missing gene</p><p>expression data. Proceedings - 2015 IEEE 14th International Conference on</p><p>Machine Learning and Applications, ICMLA 2015, 12511256.</p><p>https://doi.org/10.1109/ICMLA.2015.29</p><p></p><p>Grace-Martin, K. (2013). Assessing the Fit of Regression Models. The Analysis</p><p>Factor. https://www.theanalysisfactor.com/assessing-the-fit-of-regressionmodels/</p><p></p><p>Graham, J. W. (2009). Missing data analysis: Making it work in the real world.</p><p>Annual Review of Psychology, 60, 549576.</p><p>https://doi.org/10.1146/annurev.psych.58.110405.085530</p><p></p><p>Gupta, P. (2017). Cross-Validation in Machine Learning. Towars Data Science.</p><p>https://towardsdatascience.com/cross-validation-in-machine-learning-</p><p>72924a69872f</p><p></p><p>Hadipour, S., Harun, S., Arefnia, A., & Alamgir, M. (2016). Transfer function models</p><p>for statistical downscaling of monthly precipitation. Jurnal Teknologi, 78(94),</p><p>5562. https://doi.org/10.11113/jt.v78.9695</p><p></p><p>Halik, G., Anwar, N., Santosa, B., & Edijatno. (2015). Reservoir inflow prediction</p><p>under GCM scenario downscaled by wavelet transform and support vector</p><p>machine hybrid models. Advances in Civil Engineering, 2015(July).</p><p>https://doi.org/10.1155/2015/515376</p><p></p><p>Hamidi, O., Poorolajal, J., Sadeghifar, M., Abbasi, H., Maryanaji, Z., Faridi, H. R., &</p><p>Tapak, L. (2015). A comparative study of support vector machines and artificial</p><p>neural networks for predicting precipitation in Iran. Theoretical and Applied</p><p>Climatology, 119(34), 723731. https://doi.org/10.1007/s00704-014-1141-z</p><p></p><p>Han, M., & Zhao, Y. (2010). Robust relevance vector machine with noise variance</p><p>coefficient. Proceedings of the International Joint Conference on Neural</p><p>Networks. https://doi.org/10.1109/IJCNN.2010.5596989</p><p></p><p>Hannah, L. (2015). The Climate System and Climate Change. In Climate Change</p><p>Biology. https://doi.org/10.1016/b978-0-12-420218-4.00002-0</p><p></p><p>Hasan, N., Nath, N. C., & Rasel, R. I. (2016). A support vector regression model for</p><p>forecasting rainfall. 2nd International Conference on Electrical Information and</p><p>Communication Technologies, EICT 2015, Eict, 554559.</p><p>https://doi.org/10.1109/EICT.2015.7392014</p><p></p><p>Hayati Rezvan, P., Lee, K. J., & Simpson, J. A. (2015). The rise of multiple</p><p>imputation: A review of the reporting and implementation of the method in</p><p>medical research Data collection, quality, and reporting. BMC Medical Research</p><p>Methodology, 15(1), 114. https://doi.org/10.1186/s12874-015-0022-1</p><p></p><p>Heitjan, D. F., Rubin, D. B., Heitjan, B. Y. D. F., & Rubin, D. B. (1991). Ignorability</p><p>and Coarse Data. The Annals of Statistics, 19(4), 22442253.</p><p></p><p>Henn, B., Raleigh, M. S., Fisher, A., & Lundquist, J. D. (2013). A comparison of</p><p>methods for filling gaps in hourly near-surface air temperature data. Journal of</p><p>Hydrometeorology, 14(3), 929945. https://doi.org/10.1175/JHM-D-12-027.1</p><p></p><p>Hewitson, B. C., & Crane, R. G. (1996). Climate downscaling: Techniques and</p><p>application. Climate Research, 7(2), 8595. https://doi.org/10.3354/cr007085</p><p></p><p>Hjelmfelt, A. T., & Wang, M. (1993). Predicting Runoff using Artificial Neural</p><p>Networks. Proceedings of the International Conference on Hydrology and Water</p><p>Resources, 16(December), 233244. https://doi.org/10.1007/978-94-011-0389-</p><p>3_16</p><p></p><p>Hoi, S. C. H., Jin, R., Zhu, J., & Lyu, M. R. (2009). Semisupervised SVM batch mode</p><p>active learning with applications to image retrieval. ACM Transactions on</p><p>Information Systems, 27(3), 129. https://doi.org/10.1145/1508850.1508854</p><p></p><p>Hong, S., & Lynn, H. S. (2020). Accuracy of random-forest-based imputation of</p><p>missing data in the presence of non-normality, non-linearity, and interaction.</p><p>BMC Medical Research Methodology, 20(1), 112.</p><p>https://doi.org/10.1186/s12874-020-01080-1</p><p></p><p>Hotelling, H. (1933). Analysis of a complex of statistical variables into principal</p><p>component. Journal of Educational Psychology, 24(6), 417.</p><p></p><p>Hou, K., Shao, G., Wang, H., Zheng, L., Zhang, Q., Wu, S., & Hu, W. (2018).</p><p>Research on practical power system stability analysis algorithm based on</p><p>modified SVM. Protection and Control of Modern Power Systems, 3(1).</p><p>https://doi.org/10.1186/s41601-018-0086-0</p><p></p><p>Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2016). A Practical Guide to Support Vector</p><p>Classification. Department of Computer Science NAtional Taiwan University,</p><p>106. https://doi.org/10.1177/02632760022050997</p><p></p><p>Huang, S., Nianguang, C. A. I., Penzuti Pacheco, P., Narandes, S., Wang, Y., &</p><p>Wayne, X. U. (2018). Applications of support vector machine (SVM) learning in</p><p>cancer genomics. Cancer Genomics and Proteomics, 15(1), 4151.</p><p>https://doi.org/10.21873/cgp.20063</p><p></p><p>Hunt, L. A. (2017). Missing data imputation and its effect on the accuracy of</p><p>classification. Studies in Classification, Data Analysis, and Knowledge</p><p>Organization, 195089, 314. https://doi.org/10.1007/978-3-319-55723-6_1</p><p></p><p>Hussain, M., Yusof, K. W., Mustafa, M. R., & Afshar, N. R. (2015). Application of</p><p>statistical downscaling model (SDSM) for long term prediction of rainfall in</p><p>Sarawak, Malaysia. Water Resources Management VIII, 1, 269278.</p><p>https://doi.org/10.2495/wrm150231</p><p></p><p>I, W., & Rahman S, S. S. U. (2015). Treatment of Missing Values in Data Mining.</p><p>Journal of Computer Science & Systems Biology, 09(02), 5153.</p><p>https://doi.org/10.4172/jcsb.1000221</p><p></p><p>Idri, A., Abnane, I., & Abran, A. (2015). Systematic mapping study of missing values</p><p>techniques in software engineering data. 2015 IEEE/ACIS 16th International</p><p>Conference on Software Engineering, Artificial Intelligence, Networking and</p><p>Parallel/Distributed Computing, SNPD 2015 - Proceedings.</p><p>https://doi.org/10.1109/SNPD.2015.7176280</p><p></p><p>Irawan, N. D., Wijono, W., & Setyawati, O. (2017). Perbaikan Missing value</p><p>Menggunakan Pendekatan Korelasi Pada Metode K-Nearest Neighbor. Jurnal</p><p>Infotel, 9(3). https://doi.org/10.20895/infotel.v9i3.286</p><p></p><p>Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random</p><p>survival forests. Annals of Applied Statistics, 2(3), 841860.</p><p>https://doi.org/10.1214/08-AOAS169</p><p></p><p>Janecek, A., Gansterer, W. N. W., Demel, M., & Ecker, G. (2008). On the</p><p>Relationship Between Feature Selection and Classification Accuracy. Fsdm, 4,</p><p>90105.</p><p></p><p>Jemain, A. A. (2015). Penyurihan Ikhtisas Data Hujan. Dewan Bahasa dan Pustaka.</p><p></p><p>Jiang, P., & Chen, J. (2016). Displacement prediction of landslide based on</p><p>generalized regression neural networks with K-fold cross-validation.</p><p>Neurocomputing, 198, 4047. https://doi.org/10.1016/j.neucom.2015.08.118</p><p></p><p>Jollife, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent</p><p>developments. Philosophical Transactions of the Royal Society A: Mathematical,</p><p>Physical and Engineering Sciences, 374(2065).</p><p>https://doi.org/10.1098/rsta.2015.0202</p><p></p><p>Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and</p><p>prospects. Science, 349(6245), 255260. https://doi.org/10.1126/science.aaa8415</p><p></p><p>Joseph, V. R., & Vakayil, A. (2021). SPlit: An Optimal Method for Data Splitting.</p><p>Technometrics, 0(0), 111. https://doi.org/10.1080/00401706.2021.1921037</p><p></p><p>Journe, M., Nesterov, Y., Richtrik, P., & Sepulchre, R. (2010). Generalized power</p><p>method for sparse principal component analysis. Journal of Machine Learning</p><p>Research, 11, 517553.</p><p></p><p>Juvonen, A., Sipola, T., & Hmlinen, T. (2015). Online anomaly detection using</p><p>dimensionality reduction techniques for HTTP log analysis. Computer Networks,</p><p>91, 4656. https://doi.org/10.1016/j.comnet.2015.07.019</p><p></p><p>Kriinen, M. (2006). Semi-supervised model selection based on cross-validation.</p><p>IEEE International Conference on Neural Networks - Conference Proceedings,</p><p>18941899. https://doi.org/10.1109/ijcnn.2006.246911</p><p></p><p>Kabanda, T., & Nenwiini, S. (2016). Impacts of climate variation on the length of the</p><p>rainfall season: an analysis of spatial patterns in North-East South Africa.</p><p>Theoretical and Applied Climatology, 125(12), 93100.</p><p>https://doi.org/10.1007/s00704-015-1498-7</p><p></p><p>Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis.</p><p>Psychometrika, 23(3), 187200. https://doi.org/10.1007/BF02289233</p><p></p><p>Kaiser, J. (2014). Dealing with Missing Values in Data. Journal of Systems</p><p>Integration, 4251. https://doi.org/10.20470/jsi.v5i1.178</p><p></p><p>Kamaruzaman, I. F., Wan Zin, W. Z., & Mohd Ariff, N. (2017). A comparison of</p><p>method for treating missing daily rainfall data in Peninsular Malaysia. Malaysian</p><p>Journal of Fundamental and Applied Sciences, 13(41), 375380.</p><p>https://doi.org/10.11113/mjfas.v13n4-1.781</p><p></p><p>Kamble, V. B., & Deshmukh, S. N. (2017). Comparision Between Accuracy and</p><p>MSE,RMSE by Using Proposed Method with Imputation Technique. Oriental</p><p>Journal of Computer Science and Technology, 10(04), 773779.</p><p>https://doi.org/10.13005/ojcst/10.04.11</p><p></p><p>Kang, H. (2013). The prevention and handling of the missing data. Korean Journal of</p><p>Anesthesiology, 64(5), 402406. https://doi.org/10.4097/kjae.2013.64.5.402</p><p></p><p>Karamizadeh, S., Abdullah, S. M., Halimi, M., Shayan, J., & Rajabi, M. J. (2014).</p><p>Advantage and drawback of support vector machine functionality. I4CT 2014 -</p><p>1st International Conference on Computer, Communications, and Control</p><p>Technology, Proceedings, I4ct, 6365.</p><p>https://doi.org/10.1109/I4CT.2014.6914146</p><p></p><p>Karunanithi, N., Grenney, W. J., Whitley, D., & Bovee, K. (1995). Neural networks</p><p>for river flow prediction. Journal of Computing in Civil Engineering, 8(2), 201</p><p>220. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(293.x)</p><p></p><p>Kassambara. (2018). Evaluation of Classification Model Accuracy: Essentials.</p><p>Statistical Tools for High-Throughput Data Analysis (STHDA).</p><p>http://www.sthda.com/english/articles/36-classification-methods-essentials/143-</p><p>evaluation-of-classification-model-accuracy-essentials/</p><p></p><p>Katal, A., Wazid, M., & Goundar, R. (2013). Big Data: Issues, Challenges, Tools and</p><p>Good Practices. 2013 Sixth International Conference on Contemporary</p><p>Computing (IC3), 404409. https://doi.org/10.1109/IC3.2013.6612229.</p><p></p><p>Kavitha, R., & Kannan, E. (2016). An efficient framework for heart disease</p><p>classification using feature extraction and feature selection technique in data</p><p>mining. 1st International Conference on Emerging Trends in Engineering,</p><p>Technology and Science, ICETETS 2016 - Proceedings.</p><p>https://doi.org/10.1109/ICETETS.2016.7603000</p><p></p><p>Khan, F. U. F., Khan, K. U. Z., & Singh, S. K. (2018). Is Group Means Imputation</p><p>Any Better Than Mean Imputation: A Study Using C5.0 Classifier. Journal of</p><p>Physics: Conference Series, 1060(1), 15. https://doi.org/10.1088/1742-</p><p>6596/1060/1/012014</p><p></p><p>Kim, J., & Ryu, J. H. (2016). A heuristic gap filling method for daily precipitation</p><p>series. Water Resources Management, 30(7), 22752294.</p><p>https://doi.org/10.1007/s11269-016-1284-z</p><p></p><p>Knoben, W. J. M., Freer, J. E., & Woods, R. A. (2019). Technical note: Inherent</p><p>benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores.</p><p>Hydrology and Earth System Sciences, 23(10), 43234331.</p><p>https://doi.org/10.5194/hess-23-4323-2019</p><p></p><p>Koch, P., Konen, W., Flasch, O., & Bartz-Beielstein, T. (n.d.). Optimization of</p><p>Support Vector Regression Models for Stormwater Prediction. 146--160.</p><p></p><p>Kohavi, R. (1995). A Study of Cross-Validation and Bootstrap for Accuracy</p><p>Estimation and Model Selection. International Joint Conference of Artificial</p><p>Intelligence, March 2001.</p><p></p><p>Kolmogorov, A. N. (1957). On the representation of continuous functions of several</p><p>variables as superpositions of continuous functions of one variable and addition.</p><p>Doklady Akademii Nauk SSSR, 114(5), 953956. https://doi.org/10.18411/lj-12-</p><p>2018-148</p><p></p><p>Kong, D., Chen, Y., Li, N., Duan, C., Lu, L., & Chen, D. (2019). Relevance vector</p><p>machine for tool wear prediction. Mechanical Systems and Signal Processing,</p><p>127, 573594. https://doi.org/10.1016/j.ymssp.2019.03.023</p><p></p><p>Kong, Q., Gong, H., Ding, X., & Hou, R. (2017). Classification Application Based on</p><p>Mutual Information and Random Forest Method for High Dimensional Data.</p><p>Proceedings - 9th International Conference on Intelligent Human-Machine</p><p>Systems and Cybernetics, IHMSC 2017, 1(Mi), 171174.</p><p>https://doi.org/10.1109/IHMSC.2017.45</p><p></p><p>Kotu, V., & Deshpande, B. (2019). Model Evaluation. Data Science, 263279.</p><p>https://doi.org/10.1016/b978-0-12-814761-0.00008-3</p><p></p><p>Kouhestani, S., Eslamian, S. S., Abedi-Koupai, J., & Besalatpour, A. A. (2016).</p><p>Projection of climate change impacts on precipitation using soft-computing</p><p>techniques: A case study in Zayandeh-rud Basin, Iran. Global and Planetary</p><p>Change, 144(July), 158170. https://doi.org/10.1016/j.gloplacha.2016.07.013</p><p></p><p>Kumar, P. S., Praveen, T. V., & Prasad, M. A. (2016). Artificial Neural Network</p><p>Model for Rainfall-Runoff -A Case Study. International Journal of Hybrid</p><p>Information Technology, 9(3), 263272.</p><p>https://doi.org/10.14257/ijhit.2016.9.3.24</p><p></p><p>Lang, K. M., & Little, T. D. (2018). Principled missing data treatments. Prevention</p><p>Science, 19(3), 284294. https://doi.org/10.1007/s11121-016-0644-5</p><p></p><p>Larson, S. C. (1931). The shrinkage of the coefficient of multiple correlation. Journal</p><p>of Educational Psychology, 22(1), 4555. https://doi.org/10.1037/h0072400</p><p></p><p>Lee, K. J., & Carlin, J. B. (2010). Multiple imputation for missing data: Fully</p><p>conditional specification versus multivariate normal imputation. American</p><p>Journal of Epidemiology, 171(5), 624632. https://doi.org/10.1093/aje/kwp425</p><p></p><p>Lei, J. (2019). Cross-Validation With Confidence. Journal of the American Statistical</p><p>Association, 115(532), 19781997.</p><p>https://doi.org/10.1080/01621459.2019.1672556</p><p></p><p>Li, L., He, S., Zhang, J., & Ran, B. (2016). Short-term highway traffic flow prediction</p><p>based on a hybrid strategy considering temporalspatial information. Journal of</p><p>Advanced Transportation, 50(8), 20292040. https://doi.org/10.1002/atr.1443</p><p></p><p>Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R</p><p>News, 2(3), 1822.</p><p></p><p>Lin, S., Zhang, S., Qiao, J., Liu, H., & Yu, G. (2008). A parameter choosing method</p><p>of SVR for time series prediction. Proceedings of the 9th International</p><p>Conference for Young Computer Scientists, ICYCS 2008, 130135.</p><p>https://doi.org/10.1109/ICYCS.2008.393</p><p></p><p>Lionello, P., Abrantes, F., Congedi, L., Dulac, F., Gacic, M., Gomis, D., Goodess, C.,</p><p>Hoff, H., Kutiel, H., Luterbacher, J., Planton, S., Reale, M., Schrder, K.,</p><p>Vittoria Struglia, M., Toreti, A., Tsimplis, M., Ulbrich, U., & Xoplaki, E. (2012).</p><p>Introduction: Mediterranean Climate-Background Information. In The Climate of</p><p>the Mediterranean Region. Elsevier. https://doi.org/10.1016/B978-0-12-416042-</p><p>2.00012-4</p><p></p><p>Liu, C. W., Lin, K. H., & Kuo, Y. M. (2003). Application of factor analysis in the</p><p>assessment of groundwater quality in a blackfoot disease area in Taiwan. Science</p><p>of the Total Environment, 313(13), 7789. https://doi.org/10.1016/S0048-</p><p>9697(02)00683-6</p><p></p><p>Lo Presti, R., Barca, E., & Passarella, G. (2010). A methodology for treating missing</p><p>data applied to daily rainfall data in the Candelaro River Basin (Italy).</p><p>Environmental Monitoring and Assessment, 160(14), 122.</p><p>https://doi.org/10.1007/s10661-008-0653-3</p><p></p><p>Lopez, C., Tucker, S., Salameh, T., & Tucker, C. (2018). An unsupervised machine</p><p>learning method for discovering patient clusters based on genetic signatures.</p><p>Journal of Biomedical Informatics, 85(June), 3039.</p><p>https://doi.org/10.1016/j.jbi.2018.07.004</p><p></p><p>Loyola R, D. G., Pedergnana, M., & Gimeno Garca, S. (2016). Smart sampling and</p><p>incremental function learning for very large high dimensional data. Neural</p><p>Networks, 78, 7587. https://doi.org/10.1016/j.neunet.2015.09.001</p><p></p><p>Luo, J., & Sun, Y. (2020). Optimization of process parameters for the minimization of</p><p>surface residual stress in turning pure iron material using central composite</p><p>design. Measurement: Journal of the International Measurement Confederation,</p><p>163, 108001. https://doi.org/10.1016/j.measurement.2020.108001</p><p></p><p>MacKay, D. J. C. (1996). Bayesian Methods for Backpropagation Networks. Physics</p><p>of Neural Networks, 211254. https://doi.org/10.1007/978-1-4612-0723-8_6</p><p></p><p>Mahmood, B. (2016). 4 Reasons Your Machine Learning Model is Wrong (and How</p><p>to Fix It). KD Nuggets. https://www.kdnuggets.com/2016/12/4-reasons-machinelearning-</p><p>model-wrong.html</p><p></p><p>Majumder, S. K., Ghosh, N., & Gupta, P. K. (2005). Relevance vector machine for</p><p>optical diagnosis of cancer. Lasers in Surgery and Medicine, 36(4), 323333.</p><p>https://doi.org/10.1002/lsm.20160</p><p></p><p>Malhi, A., & Gao, R. X. (2004). PCA-based feature selection scheme for machine</p><p>defect classification. IEEE Transactions on Instrumentation and Measurement,</p><p>53(6), 15171525. https://doi.org/10.1109/TIM.2004.834070</p><p></p><p>Mandel J, S. P. (2015). A Comparison of Six Methods for Missing Data Imputation.</p><p>Journal of Biometrics & Biostatistics, 06(01), 16. https://doi.org/10.4172/2155-</p><p>6180.1000224</p><p></p><p>Manikandan, J., & Venkataramani, B. (2009). Design of a modified one-against-all</p><p>SVM classifier. Conference Proceedings - IEEE International Conference on</p><p>Systems, Man and Cybernetics, October, 18691874.</p><p>https://doi.org/10.1109/ICSMC.2009.5346200</p><p></p><p>Marais, J., Dreuzy, J. De, Marais, J., Prospective, J. D. D., & Learning, D. (2018).</p><p>Prospective Interest of Deep Learning for Hydrological Inference. Groundwater,</p><p>Wiley, 55(5), 688692. https://hal-insu.archives-ouvertes.fr/insu-01574652</p><p></p><p>McCuen, R. H., Knight, Z., & Cutter, A. G. (2006). Evaluation of the NashSutcliffe</p><p>Efficiency Index. Journal of Hydrologic Engineering, 11(6), 597602.</p><p>https://doi.org/10.1061/(asce)1084-0699(2006)11:6(597)</p><p></p><p>Mechoso, C. R., & Arakawa, A. (2015). Numerical Models: General Circulation</p><p>Models. In Encyclopedia of Atmospheric Sciences: Second Edition (Second Edi,</p><p>Vol. 4). Elsevier. https://doi.org/10.1016/B978-0-12-382225-3.00157-2</p><p></p><p>Mehta, P., Bukov, M., Wang, C. H., Day, A. G. R., Richardson, C., Fisher, C. K., &</p><p>Schwab, D. J. (2019). A high-bias, low-variance introduction to Machine</p><p>Learning for physicists. Physics Reports, 810, 1124.</p><p>https://doi.org/10.1016/j.physrep.2019.03.001</p><p></p><p>Mekonnen, D. G., Moges, M. A., Mulat, A. G., & Shumitter, P. (2019). The impact of</p><p>climate change on mean and extreme state of hydrological variables in Megech</p><p>watershed, Upper Blue Nile Basin, Ethiopia. In Extreme Hydrology and Climate</p><p>Variability: Monitoring, Modelling, Adaptation and Mitigation (Issue 2009).</p><p>Elsevier Inc. https://doi.org/10.1016/B978-0-12-815998-9.00011-7</p><p></p><p>Meng, C., Zeleznik, O. A., Thallinger, G. G., Kuster, B., Gholami, A. M., & Culhane,</p><p>A. C. (2016). Dimension reduction techniques for the integrative analysis of</p><p>multi-omics data. Briefings in Bioinformatics, 17(4), 628641.</p><p>https://doi.org/10.1093/bib/bbv108</p><p></p><p>Methaprayoon, K., Yingvivatanapong, C., Lee, W. J., & Liao, J. R. (2007). An</p><p>integration of ANN wind power estimation into unit commitment considering the</p><p>forecasting uncertainty. IEEE Transactions on Industry Applications, 43(6),</p><p>14411448. https://doi.org/10.1109/TIA.2007.908203</p><p></p><p>Minakshi Vohra, R. G. (2014). Missing Value Imputation in Multi Attribute Data Set.</p><p>International Journal of Computer Science and Information Technologies, 5(4),</p><p>53155321.</p><p></p><p>Mishra, N., Soni, H. K., Sharma, S., & Upadhyay, A. K. (2018). Development and</p><p>analysis of Artificial Neural Network models for rainfall prediction by using</p><p>time-series data. International Journal of Intelligent Systems and Applications,</p><p>10(1), 1623. https://doi.org/10.5815/ijisa.2018.01.03</p><p></p><p>Mishra, S., & Datta-Gupta, A. (2018). Data-Driven Modeling. Applied Statistical</p><p>Modeling and Data Analytics, 195224. https://doi.org/10.1016/b978-0-12-</p><p>803279-4.00008-0</p><p></p><p>Moritz, S., Sard, A., Bartz-Beielstein, T., Zaefferer, M., & Stork, J. (2015).</p><p>Comparison of different Methods for Univariate Time Series Imputation in R.</p><p>Preprint ArXiv:1510.03924, arXiv, 120. http://arxiv.org/abs/1510.03924</p><p></p><p>Moss, H. B., Leslie, D. S., & Rayson, P. (2018). Using J-K-fold cross validation to</p><p>reduce variance when tuning NLP models. ArXiv.</p><p></p><p>Mosteller, F., & Tukey, J. W. (1968). Data analysis, including statistics. In Handbook</p><p>of Social Psychology. Addison-Wesley. https://doi.org/10.1214/aos/1043351253</p><p></p><p>Mosteller, Frederick, & Wallace, D. L. (1963). Inference in an Authorship Problem.</p><p>Journal of the American Statistical Association, 58(302), 275309.</p><p>https://doi.org/10.1080/01621459.1963.10500849</p><p></p><p>Mubarak, S., Darwis, H., Umar, F., Ilmawan, L. B., Anraeni, S., & Mude, M. A.</p><p>(2018). Feature Selection of Oral Cyst and Tumor Images Using Principal</p><p>Component Analysis. Proceedings - 2nd East Indonesia Conference on</p><p>Computer and Information Technology: Internet of Things for Industry,</p><p>EIConCIT 2018, 322325. https://doi.org/10.1109/EIConCIT.2018.8878641</p><p></p><p>Muhammad, I., & Yan, Z. (2015). Supervised Machine Learning Approaches: a</p><p>Survey. ICTACT Journal on Soft Computing, 05(03), 946952.</p><p>https://doi.org/10.21917/ijsc.2015.0133</p><p></p><p>Murti, D. M. P., Pujianto, U., Wibawa, A. P., & Akbar, M. I. (2019). K-Nearest</p><p>Neighbor (K-NN) based Missing Data Imputation. Proceeding - 2019 5th</p><p>International Conference on Science in Information Technology: Embracing</p><p>Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019, 83</p><p>88. https://doi.org/10.1109/ICSITech46713.2019.8987530</p><p></p><p>Naik, P., Wedel, M., Bacon, L., Bodapati, A., Bradlow, E., Kamakura, W., Kreulen,</p><p>J., Lenk, P., Madigan, D. M., & Montgomery, A. (2008). Challenges and</p><p>opportunities in high-dimensional choice data analyses. Marketing Letters, 19(3</p><p>4), 201213. https://doi.org/10.1007/s11002-008-9036-3</p><p></p><p>Nanda, M. A., Seminar, K. B., Nandika, D., & Maddu, A. (2018). A comparison study</p><p>of kernel functions in the support vector machine and its application for termite</p><p>detection. Information (Switzerland), 9(1). https://doi.org/10.3390/info9010005</p><p></p><p>Nash, J. E., & Sutcliffe, J. V. (1970). River Flow Forecasting through Conceptual</p><p>Models Part 1- A discussion of principles. In Journal of Hydrology (Vol. 10,</p><p>Issue 3). https://doi.org/10.1080/00750770109555783</p><p></p><p>Nasteski, V. (2017). An overview of the supervised machine learning methods.</p><p>Horizons.B, 4(December 2017), 5162.</p><p>https://doi.org/10.20544/horizons.b.04.1.17.p05</p><p></p><p>Nasution, M. Z. F., Sitompul, O. S., & Ramli, M. (2018). PCA based feature</p><p>reduction to improve the accuracy of decision tree c4.5 classification. Journal of</p><p>Physics: Conference Series, 978(1). https://doi.org/10.1088/1742-</p><p>6596/978/1/012058</p><p></p><p>Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipfs law.</p><p>Contemporary Physics, 46(5), 323351.</p><p>https://doi.org/10.1080/00107510500052444</p><p></p><p>Ng, S. C. (2017). Principal component analysis to reduce dimension on digital image.</p><p>Procedia Computer Science, 111(2015), 113119.</p><p>https://doi.org/10.1016/j.procs.2017.06.017</p><p></p><p>Nikolaev, N., & Tino, P. (2005). Sequential relevance vector machine learning from</p><p>time series. Proceedings of the International Joint Conference on Neural</p><p>Networks, 2, 13081313. https://doi.org/10.1109/IJCNN.2005.1556043</p><p></p><p>Nishijima, M., Nieuwenhoff, N., Pires, R., & Oliveira, P. R. (2019). Movie films</p><p>consumption in Brazil: an analysis of support vector machine classification. AI</p><p>and Society, 0123456789. https://doi.org/10.1007/s00146-019-00899-7</p><p></p><p>Noor, M., Tarmizi Ismail, S. S., Bin, F. A. B., Nashwan, M. S., Khan, N., Ahmed, K.,</p><p>Shiru, M. S., Muhammad, M. K. I. Bin, A.Salman, S., Momade, M. H., Iqbal, Z.,</p><p>SaAdi, Z., & Khan, and S. U. (n.d.). Annual Rainfall Variations in Peninsular</p><p>Malaysia under Climate Change Scenarios. 1(15), 298317.</p><p></p><p>Nourani, V., Razzaghzadeh, Z., Baghanam, A. H., & Molajou, A. (2019). ANN-based</p><p>statistical downscaling of climatic parameters using decision tree predictor</p><p>screening method. Theoretical and Applied Climatology, 137(34), 17291746.</p><p>https://doi.org/10.1007/s00704-018-2686-z</p><p></p><p>O. Yamini, & Prof. S. Ramakrishna. (2015). A Study on Advantages of Data Mining</p><p>Classification Techniques. International Journal of Engineering Research And,</p><p>V4(09), 969972. https://doi.org/10.17577/ijertv4is090815</p><p></p><p>Okkan, U., & Inan, G. (2015). Bayesian Learning and Relevance Vector Machines</p><p>Approach for Downscaling of Monthly Precipitation. Journal of Hydrologic</p><p>Engineering, 20(4), 04014051. https://doi.org/10.1061/(asce)he.1943-</p><p>5584.0001024</p><p></p><p>Okkan, U., Serbes, Z. A., & Samui, P. (2014). Relevance vector machines approach</p><p>for long-term flow prediction. Neural Computing and Applications, 25(6), 1393</p><p>1405. https://doi.org/10.1007/s00521-014-1626-9</p><p></p><p>Othman, A. S., & Tukimat, N. N. A. (2018). Assessment of the Potential Occurrence</p><p>of Dry Period in the Long Term for Pahang State, Malaysia. MATEC Web of</p><p>Conferences, 150, 16. https://doi.org/10.1051/matecconf/201815003004</p><p></p><p>Pal, M. (2011). Kernel Methods in Remote Sensing: A review. Ish Journal of</p><p>Hydraulic Engineering, 15(1), 194215. http://arxiv.org/abs/1101.2987</p><p></p><p>Panigrahi, R., & Borah, S. (2019). Classification and Analysis of Facebook Metrics</p><p>Dataset Using Supervised Classifiers. In Social Network Analytics. Elsevier Inc.</p><p>https://doi.org/10.1016/b978-0-12-815458-8.00001-3</p><p></p><p>Pantanowitz, A., & Marwala, T. (2009). Missing data imputation through the use of</p><p>the random forest algorithm. Advances in Intelligent and Soft Computing, 61</p><p>AISC, 5362. https://doi.org/10.1007/978-3-642-03156-4_6</p><p></p><p>Parmar, A., Mistree, K., & Sompurna, M. (2017). Machine Learning Techniques for</p><p>Rainfall Prediction : A Review. 3(6), 913917.</p><p></p><p>Paul D., A. (2001). Missing data Quantitative applications in the social sciences.</p><p>SAGE Publication.</p><p></p><p>Pearson F.R.S., K. (1901). Llll. On lines and planes of closest fit to systems of points</p><p>in space. The London, Edinburgh, and Dublin Philosophical Magazine and</p><p>Journal of Science Series, 2(11), 559572.</p><p>https://doi.org/10.1080/14786440109462720</p><p></p><p>Pekalska, E. (2015). Pattern Recognition Tools. Pattern Recognition Tools 37Steps.</p><p>http://37steps.com/4859/cross-validation/</p><p></p><p>Pepinsky, T. B. (2018). A Note on Listwise Deletion versus Multiple Imputation.</p><p>Political Analysis, 26(4), 480488. https://doi.org/10.1017/pan.2018.18</p><p></p><p>Peterson, C., & Rognvaldsson, T. (1991). An Introduction to Artifical Neuron</p><p>Network. In Fundamental of Neural Network: Architecture Algorithm and</p><p>Application (pp. 113169). 1991 CERN School of Computing.</p><p></p><p>Pett, M., Lackey, N., & Sullivan, J. (2011). An Overview of Factor Analysis. Making</p><p>Sense of Factor Analysis, 212. https://doi.org/10.4135/9781412984898.n1</p><p></p><p>Peugh, J. L., & Enders, C. K. (2004). Missing data in educational research: A review</p><p>of reporting practices and suggestions for improvement. Review of Educational</p><p>Research, 74(4), 525556. https://doi.org/10.3102/00346543074004525</p><p></p><p>Pour, S. H., Shahid, S., & Chung, E. S. (2016). A Hybrid Model for Statistical</p><p>Downscaling of Daily Rainfall. Procedia Engineering, 154, 14241430.</p><p>https://doi.org/10.1016/j.proeng.2016.07.514</p><p></p><p>Pramoditha, R. (2021). 11 Dimensionality reduction techniques you should know in</p><p>2021. Medium. https://towardsdatascience.com/11-dimensionality-reductiontechniques-</p><p>you-should-know-in-2021-dcb9500d388b</p><p></p><p>Punlumjeak, W., Arunrerk, J., & Rachburee, N. (2017). An analytics prediction model</p><p>of monthly rainfall time series: Case of Thailand. Journal of Telecommunication,</p><p>Electronic and Computer Engineering, 9(26), 5357.</p><p></p><p>Qian, L., Liu, C., Yi, J., & Liu, S. (2020). Application of hybrid algorithm of bionic</p><p>heuristic and machine learning in nonlinear sequence. Journal of Physics:</p><p>Conference Series, 1682(1). https://doi.org/10.1088/1742-6596/1682/1/012009</p><p></p><p>Qiu, M., Song, Y., & Akagi, F. (2016). Application of artificial neural network for the</p><p>prediction of stock market returns: The case of the Japanese stock market.</p><p>Chaos, Solitons and Fractals, 85, 17.</p><p>https://doi.org/10.1016/j.chaos.2016.01.004</p><p></p><p>Qiu, S., Gao, L., & Wang, J. (2014). Classification and regression of ELM, LVQ and</p><p>SVM for E-nose data of strawberry juice. Journal of Food Engineering, 144, 77</p><p>85. https://doi.org/10.1016/j.jfoodeng.2014.07.015</p><p></p><p>Quiiionero-candela, J., & Hansen, L. K. (2002). Time Series Prediction based on the</p><p>Relevance Vector Machine with Adaptive Kernels. 2002 IEEE International</p><p>Conference on Acoustics, Speech, and Signal Processing, 985988.</p><p></p><p>Raghavendra, S., & Deka, P. C. (2014). Support vector machine applications in the</p><p>field of hydrology: A review. Applied Soft Computing Journal, 19, 372386.</p><p>https://doi.org/10.1016/j.asoc.2014.02.002</p><p></p><p>Rakesh Tanty, & Tanweer S. Desmukh. (2015). Application of Artificial Neural</p><p>Network in Hydrology- A Review. International Journal of Engineering</p><p>Research And, V4(06), 27. https://doi.org/10.17577/ijertv4is060247</p><p></p><p>Raman, H., & Sunilkumar, N. (1995). Multivariate modelling of water resources time</p><p>series using artificial neural networks. Hydrological Sciences Journal, 40(2),</p><p>145163. https://doi.org/10.1080/02626669509491401</p><p></p><p>Rau, P., Bourrel, L., Labat, D., Melo, P., Dewitte, B., Frappart, F., Lavado, W., &</p><p>Felipe, O. (2017). Regionalization of rainfall over the Peruvian Pacific slope and</p><p>coast. International Journal of Climatology, 37(1), 143158.</p><p>https://doi.org/10.1002/joc.4693</p><p></p><p>Rawal, S., Gupta, S. C., & Singh, S. (2017). Predicting Missing Values in a Dataset:</p><p>Challenges and Approaches. International Journal of Recent Research Aspects,</p><p>4(3), 3438. https://www.ijrra.net/Vol4issue3/IJRRA-04-03-07.pdf</p><p></p><p>Ray, S. (2015). 7 Regression Techniques you should know! Analytics Vidhya.</p><p>https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guideregression/</p><p></p><p>Ray, S. (2017). Understanding Support Vector Machine(SVM) algorithm from</p><p>examples (along with code). Analytics Vidhya.</p><p>https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vectormachine-</p><p>example-code/</p><p></p><p>Ries, A., Campbell, A., Strategic, A., Centre, M., & Zeldin, T. (1997). The 80/20</p><p>principle: The secret of achieving more with less. In Long Range Planning (Vol.</p><p>30, Issue 6). https://doi.org/10.1016/s0024-6301(97)80978-8</p><p></p><p>Ritter, A., & Muoz-Carpena, R. (2013). Performance evaluation of hydrological</p><p>models: Statistical significance for reducing subjectivity in goodness-of-fit</p><p>assessments. Journal of Hydrology, 480, 3345.</p><p>https://doi.org/10.1016/j.jhydrol.2012.12.004</p><p></p><p>Rodr, R., Pastorini, M., Etcheverry, L., Chreties, C., Fossati, M., Castro, A., &</p><p>Gorgoglione, A. (2021). Water-Quality Data Imputation with a High Percentage</p><p>of Missing Values : A Machine Learning Approach. Sustainability, 13, 6318.</p><p></p><p>Rohani, A., Taki, M., & Abdollahpour, M. (2018). A novel soft computing model</p><p>(Gaussian process regression with K-fold cross validation) for daily and monthly</p><p>solar radiation forecasting (Part: I). Renewable Energy, 115, 411422.</p><p>https://doi.org/10.1016/j.renene.2017.08.061</p><p></p><p>Rosebrock, A. (2019). Why is my validation loss lower than my training loss?</p><p>PyImageSearch. https://www.pyimagesearch.com/2019/10/14/why-is-myvalidation-</p><p>loss-lower-than-my-training-loss/</p><p></p><p>Roudier, P. (2017). Just enough machine learning to be dangerous. Creative</p><p>Commons Attribution 4.0. http://pierreroudier.github.io/teaching/20171014-</p><p>DSM-Masterclass-Hamilton/machine-learningtheory.</p><p>html#for_more_information</p><p></p><p>Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581592.</p><p>https://doi.org/10.1186/1471-2105-12-432</p><p></p><p>Rubin, D. B., & Wiley, A. J. (2014). Statistical Analysis with Missing Data. NY: John</p><p>Wiley & Sons.</p><p></p><p>Ruiming, F. (2019). Wavelet based relevance vector machine model for monthly</p><p>runoff prediction. Water Quality Research Journal of Canada, 54(2), 134141.</p><p>https://doi.org/10.2166/wcc.2018.196</p><p></p><p>Rushton, A., Croucher, P. and Baker, P. (2014). Handbook of logistics and</p><p>distribution management. Kogan Page Limited.</p><p></p><p>Sachindra, D. A., Ahmed, K., Rashid, M. M., Shahid, S., & Perera, B. J. C. (2018).</p><p>Statistical downscaling of precipitation using machine learning techniques.</p><p>Atmospheric Research, 212, 240258.</p><p>https://doi.org/10.1016/j.atmosres.2018.05.022</p><p></p><p>Sachindra, D. A., Huang, F., Barton, A., & Perera, B. J. C. (2013). Least square</p><p>support vector and multi-linear regression for statistically downscaling general</p><p>circulation model outputs to catchment streamflows. International Journal of</p><p>Climatology, 33(5), 10871106. https://doi.org/10.1002/joc.3493</p><p></p><p>Saiful Samsudin, M., Azid, A., Iskandar Khalit, S., Milleana Shaharudin, S., Lananan,</p><p>F., & Juahir, H. (2018). Pollution Sources Identification of Water Quality Using</p><p>Chemometrics: a Case Study in Klang River Basin, Malaysia. International</p><p>Journal of Engineering & Technology, 7(4.43), 8389.</p><p>https://www.researchgate.net/publication/331701453</p><p></p><p>Saini, O. and P. S. S. (2018). A Review on Dimension Reduction Techniques in Data</p><p>Mining. Computer Engineering and Intelligent Systems, 9(1), 714.</p><p></p><p>Saitta, S. (2010). What is a good classification accuracy in data mining? Data</p><p>Mining. http://www.dataminingblog.com/what-is-a-good-classificationaccuracy-</p><p>in-data-mining/</p><p></p><p>Salvi, K., S., K., & Ghosh, S. (2013). High-resolution multisite daily rainfall</p><p>projections in India with statistical downscaling for climate change impacts</p><p>assessment. Journal of Geophysical Research: Atmospheres, 118(9), 35573578.</p><p>https://doi.org/10.1002/jgrd.50280</p><p></p><p>Samsudin, M. S., Khalit, S. I., Azid, A., Juahir, H., Mohd Saudi, A. S., Sharip, Z., &</p><p>Zaudi, M. A. (2017). Control limit detection for source apportionment in Perlis</p><p>River Basin, Malaysia. Malaysian Journal of Fundamental and Applied</p><p>Sciences, 13(3). https://doi.org/10.11113/mjfas.v13n3.687</p><p></p><p>Samui, P. (2012). Application of Relevance Vector Machine for Prediction of</p><p>Ultimate Capacity of Driven Piles in Cohesionless Soils. Geotechnical and</p><p>Geological Engineering, 30(5), 12611270. https://doi.org/10.1007/s10706-012-</p><p>9539-9</p><p></p><p>Samui, P., & Dixon, B. (2012). Application of support vector machine and relevance</p><p>vector machine to determine evaporative losses in reservoirs. Hydrological</p><p>Processes, 26(9), 13611369. https://doi.org/10.1002/hyp.8278</p><p></p><p>Samui, P., Mandla, V. R., Krishna, A., & Teja, T. (2011). Prediction of Rainfall Using</p><p>Support Vector Machine and Relevance Vector Machine. Earth Science India,</p><p>4(Iv), 188200.</p><p></p><p>Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art.</p><p>Psychological Methods, 7(2), 147177. https://doi.org/10.1037/1082-</p><p>989X.7.2.147</p><p></p><p>Schmidt, I., & Mosima, B. (2014). To Impute or Not Impute : That Is the Question ?</p><p>In & H. J. A. (Eds. . In G. J. Mellenbergh (Ed.), Advising on research methods:</p><p>Selected topics 2013. Johannes van Kessel Publishing.</p><p>http://www.paultwin.com/wpcontent/</p><p>uploads/Lodder_1140873_Paper_Imputation.pdf</p><p></p><p>Schlkopf, B., Smola, A., & Mller, K. R. (1997). Kernel principal component</p><p>analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes</p><p>in Artificial Intelligence and Lecture Notes in Bioinformatics), 1327, 583588.</p><p></p><p>Schoof, J. T. (2013). Statistical downscaling in climatology. Geography Compass,</p><p>7(4), 249265. https://doi.org/10.1111/gec3.12036</p><p></p><p>Shaharudin, S. M., Ahmad, N., Zainuddin, N. H., & Mohamed, N. S. (2018).</p><p>Identification of rainfall patterns on hydrological simulation using robust</p><p>principal component analysis. Indonesian Journal of Electrical Engineering and</p><p>Computer Science, 11(3), 11621167.</p><p>https://doi.org/10.11591/ijeecs.v11.i3.pp1162-1167</p><p></p><p>Shaharudin, Shazlyn Milleana, Andayani, S., Kismiantini, Binatari, N., Kurniawan,</p><p>A., Basri, M. A. A., & Zainuddin, N. H. (2020). Imputation methods for</p><p>addressing missing data of monthly rainfall in Yogyakarta, Indonesia.</p><p>International Journal of Advanced Trends in Computer Science and</p><p>Engineering, 9(1.4 Special Issue), 646651.</p><p>https://doi.org/10.30534/ijatcse/2020/9091.42020</p><p></p><p>Shamseldin, A. Y. (1997). Application of a neural network technique to rainfallrunoff</p><p>modelling. Journal of Hydrology, 199(34), 272294.</p><p>https://doi.org/10.1016/S0022-1694(96)03330-6</p><p></p><p>Sherer, T., & JiayueHu. (2018). Training and Test Data sets.</p><p>Shetty, B. (2020). An In-Depth Guide to Supervised Machine Learning Classification.</p><p>Built In. https://builtin.com/data-science/supervised-machine-learningclassification</p><p></p><p>Shi, C. R., & Adnan, R. (2014). Modified cross-validation as a method for estimating</p><p>parameter. AIP Conference Proceedings, 1635(2014), 724731.</p><p>https://doi.org/10.1063/1.4903662</p><p></p><p>Shinozaki, T., & Ostendorf, M. (2008). Cross-validation and aggregated EM training</p><p>for robust parameter estimation. Computer Speech and Language, 22(2), 185</p><p>195. https://doi.org/10.1016/j.csl.2007.07.005</p><p></p><p>Singh, K. P., Malik, A., & Sinha, S. (2005). Water quality assessment and</p><p>apportionment of pollution sources of Gomti river (India) using multivariate</p><p>statistical techniques - A case study. Analytica Chimica Acta, 538(12), 355</p><p>374. https://doi.org/10.1016/j.aca.2005.02.006</p><p></p><p>Smid, M., & Costa, A. C. (2018). Climate projections and downscaling techniques: a</p><p>discussion for impact studies in urban systems. International Journal of Urban</p><p>Sciences, 22(3), 277307. https://doi.org/10.1080/12265934.2017.1409132</p><p></p><p>Soley-bori, M. (2013). Dealing with missing data: Key assumptions and methods for</p><p>applied analysis. PM931 Directed Study in Health Policy and Management, 4,</p><p>20.</p><p></p><p>Song, F., Guo, Z., & Mei, D. (2010). Feature selection using principal component</p><p>analysis. Proceedings - 2010 International Conference on System Science,</p><p>Engineering Design and Manufacturing Informatization, ICSEM 2010, 1, 2730.</p><p>https://doi.org/10.1109/ICSEM.2010.14</p><p></p><p>Stahl, J. (2019). Overfitting in Machine Learning: What it is and How to prevent.</p><p>Elite Data Science. https://elitedatascience.com/overfitting-in-machine-learning</p><p></p><p>Stekhoven, D. J., & Bhlmann, P. (2012). Missforest-Non-parametric missing value</p><p>imputation for mixed-type data. Bioinformatics, 28(1), 112118.</p><p>https://doi.org/10.1093/bioinformatics/btr597</p><p></p><p>Stephen, O. (2012). Hybrid GA-SVM for Efficient Feature Selection in E-mail</p><p>Classification. 3(3), 1729.</p><p></p><p>Stone M. (1974). Cross-Validatory Choice and Assessment of Statistical Predictions.</p><p>Journal of the Royal Statistical Society. Series B (Methodological), 36(2), 111</p><p>147.</p><p></p><p>Stone, M. (1977). Equivalence of Choice of Model by Cross-validation An</p><p>Asymptotic Akaike s Criterion. Journal of the Royal Statistical Society. Series</p><p>B (Methodological), 39(1), 4447.</p><p></p><p>Su, Y., Huang, Y., & Kuo, C. C. J. (2018). Efficient Text Classification Using Treestructured</p><p>Multi-linear Principal Component Analysis. Proceedings -</p><p>International Conference on Pattern Recognition, 2018-Augus, 585590.</p><p>https://doi.org/10.1109/ICPR.2018.8545832</p><p></p><p>Tahir, T., Hashim, A. M., & Yusof, K. W. (2018). Statistical downscaling of rainfall</p><p>under transitional climate in Limbang River Basin by using SDSM. IOP</p><p>Conference Series: Earth and Environmental Science, 140(1).</p><p>https://doi.org/10.1088/1755-1315/140/1/012037</p><p></p><p>Tang, F., & Ishwaran, H. (2017). Random forest missing data algorithms. Statistical</p><p>Analysis and Data Mining, 10(6), 363377. https://doi.org/10.1002/sam.11348</p><p></p><p>Tang, J., Niu, X., Wang, S., Gao, H., Wang, X., & Wu, J. (2016). Statistical</p><p>downscaling and dynamical downscaling of regional climate in China: Present</p><p>climate evaluations and future climate projections. Journal of Geophysical</p><p>Research: Atmospheres, 121, 21102129. https://doi.org/10.1038/175238c0</p><p></p><p>Tangri, N., Ansell, D., & Naimark, D. (2008). Predicting technique survival in</p><p>peritoneal dialysis patients: Comparing artificial neural networks and logistic</p><p>regression. Nephrology Dialysis Transplantation, 23(9), 29722981.</p><p>https://doi.org/10.1093/ndt/gfn187</p><p></p><p>Tannenbaum, C. E. (2009). The Empirical Nature and Statistical Treatment of</p><p>Missing data [University of Pennsylvania]. In ProQuest Dissertations</p><p>Publishing. http://dx.doi.org/10.1016/j.jaci.2012.05.050</p><p></p><p>Tanwar, S., Ramani, T., & Tyagi, S. (2018). Dimensionality reduction using PCA and</p><p>SVD in big data: A comparative case study. Lecture Notes of the Institute for</p><p>Computer Sciences, Social-Informatics and Telecommunications Engineering,</p><p>LNICST, 220 LNICST, 116125. https://doi.org/10.1007/978-3-319-73712-6_12</p><p></p><p>Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping</p><p>using a novel ensemble weights-of-evidence and support vector machine models</p><p>in GIS. Journal of Hydrology, 512, 332343.</p><p>https://doi.org/10.1016/j.jhydrol.2014.03.008</p><p></p><p>Tipping, M. E. (2000). The relevance vector machine. Advances in Neural</p><p>Information Processing Systems, 653658.</p><p></p><p>Tipping, M. E. (2001). Sparse Bayesian Learning and the Relevance Vector Machine.</p><p>Journal of Machine Learning Research, 1(3), 211244.</p><p>https://doi.org/10.1162/15324430152748236</p><p></p><p>Tisseuil, C., Vrac, M., Lek, S., & Wade, A. J. (2010). Statistical downscaling of river</p><p>flows. Journal of Hydrology, 385(14), 279291.</p><p>https://doi.org/10.1016/j.jhydrol.2010.02.030</p><p></p><p>Toku, A. A. (2021). Splitting a Dataset into Train and Test Sets. Baeldung.</p><p>https://www.baeldung.com/cs/train-test-datasets-ratio</p><p></p><p>Tripathi, S., & Govindaraju, R. S. (2007). On selection of kernel parametes in</p><p>relevance vector machines for hydrologic applications. Stochastic Environmental</p><p>Research and Risk Assessment, 21(6), 747764. https://doi.org/10.1007/s00477-</p><p>006-0087-9</p><p></p><p>Tripathi, S., Srinivas, V. V., & Nanjundiah, R. S. (2006). Downscaling of</p><p>precipitation for climate change scenarios: A support vector machine approach.</p><p>Journal of Hydrology, 330(34), 621640.</p><p>https://doi.org/10.1016/j.jhydrol.2006.04.030</p><p></p><p>Trzaska, S., & Schnarr, E. (2014). A review of downscaling methods for climate</p><p>change projections. United States Agency for International Development by</p><p>Tetra Tech ARD, September, 142.</p><p></p><p>Tsakiri, K., Marsellos, A., & Kapetanakis, S. (2018). Artificial neural network and</p><p>multiple linear regression for flood prediction in Mohawk River, New York.</p><p>Water (Switzerland), 10(9). https://doi.org/10.3390/w10091158</p><p></p><p>Tutz, G., & Ramzan, S. (2015). Improved methods for the imputation of missing data</p><p>by nearest neighbor methods. Computational Statistics and Data Analysis,</p><p>90(xxxx), 8499. https://doi.org/10.1016/j.csda.2015.04.009</p><p></p><p>Valentine, J. C., & McHugh, C. M. (2007). The Effects of Attrition on Baseline</p><p>Comparability in Randomized Experiments in Education: A Meta-Analysis.</p><p>Psychological Methods, 12(3), 268282. https://doi.org/10.1037/1082-</p><p>989X.12.3.268</p><p></p><p>Vallantin, L. (2018). Why you should not trust only in accuracy to measure machine</p><p>learning performance. Medium. https://medium.com/@limavallantin/why-youshould-</p><p>not-trust-only-in-accuracy-to-measure-machine-learning-performancea72cf00b4516</p><p></p><p>van der Heijden, G. J. M. G., T. Donders, A. R., Stijnen, T., & Moons, K. G. M.</p><p>(2006). Imputation of missing values is superior to complete case analysis and</p><p>the missing-indicator method in multivariable diagnostic research: A clinical</p><p>example. Journal of Clinical Epidemiology, 59(10), 11021109.</p><p>https://doi.org/10.1016/j.jclinepi.2006.01.015</p><p></p><p>Van Heerden, C., Barnard, E., Davel, M., Van Der Walt, C., Van Dyk, E., Feld, M., &</p><p>Mller, C. (2010). Combining regression and classification methods for</p><p>improving automatic speaker age recognition. ICASSP, IEEE International</p><p>Conference on Acoustics, Speech and Signal Processing - Proceedings, 5174</p><p>5177. https://doi.org/10.1109/ICASSP.2010.5495006</p><p></p><p>Van Uytven, E., De Niel, J., & Willems, P. (2019). Uncovering the shortcomings of a</p><p>weather typing based statistical downscaling method. Hydrology and Earth</p><p>System Sciences Discussions, 135. https://doi.org/10.5194/hess-2019-40</p><p></p><p>Vandal, T., Kodra, E., & Ganguly, A. R. (2019). Intercomparison of machine learning</p><p>methods for statistical downscaling: the case of daily and extreme precipitation.</p><p>Theoretical and Applied Climatology, 137(12), 557570.</p><p>https://doi.org/10.1007/s00704-018-2613-3</p><p></p><p>Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer Science.</p><p>https://doi.org/10.1007/978-1-4757-2440-0</p><p></p><p>Visoni, P. (2015). Predictive Model Selection Criteria for Logistic Regression.</p><p>Statistical Modelling, 8, 10001005. https://doi.org/10.1400/40307</p><p></p><p>Vrac, M., Stein, M., & Hayhoe, K. (2007). Statistical downscaling of precipitation</p><p>through nonhomogeneous stochastic weather typing. Climate Research, 34(3),</p><p>169184. https://doi.org/10.3354/cr00696</p><p></p><p>Vu, M. T., Aribarg, T., Supratid, S., Raghavan, S. V., & Liong, S. Y. (2016).</p><p>Statistical downscaling rainfall using artificial neural network: significantly</p><p>wetter Bangkok? Theoretical and Applied Climatology, 126(34), 453467.</p><p>https://doi.org/10.1007/s00704-015-1580-1</p><p></p><p>Wakefield, K. (2019). Predictive analytics and machine learning. SAS Analytics.</p><p>https://www.sas.com/en_gb/insights/articles/analytics/a-guide-to-predictiveanalytics-</p><p>and-machine-learning.html</p><p></p><p>Waljee, A. K., Mukherjee, A., Singal, A. G., Zhang, Y., Warren, J., Balis, U.,</p><p>Marrero, J., Zhu, J., & Higgins, P. D. R. (2013). Comparison of imputation</p><p>methods for missing laboratory data in medicine. BMJ Open, 3(8), 17.</p><p>https://doi.org/10.1136/bmjopen-2013-002847</p><p></p><p>Wang, J. E., & Qiao, J. Z. (2014). Parameter selection of SVR based on improved kfold</p><p>cross validation. Applied Mechanics and Materials, 462463, 182186.</p><p>https://doi.org/10.4028/www.scientific.net/AMM.462-463.182</p><p></p><p>Wang, Y., Xiao, Y., Lai, J., & Chen, Y. (2020). An adaptive k nearest neighbour</p><p>method for imputation of missing traffic data based on two similarity metrics.</p><p>Archives of Transport, 54(2), 5973. https://doi.org/10.5604/01.3001.0014.2968</p><p></p><p>Wei, L., Yang, Y., Nishikawa, R. M., Wernick, M. N., & Edwards, A. (2005).</p><p>Relevance vector machine for automatic detection a of clustered</p><p>microcalcifications. IEEE Transactions on Medical Imaging, 24(10), 12781285.</p><p>https://doi.org/10.1109/TMI.2005.855435</p><p></p><p>Wen, Z., Li, B., Ramamohanarao, K., Chen, J., Chen, Y., & Zhang, R. (2017).</p><p>Improving efficiency of SVM k-fold cross-validation by alpha seeding. 31st</p><p>AAAI Conference on Artificial Intelligence, AAAI 2017, i, 27682774.</p><p></p><p>Wigley, R. L. W. and T. M. L. (1997). Downscaling general circulation model</p><p>output:a review of methods and limitations. Progress in Physical Geography,</p><p>21(4), 530548.</p><p></p><p>Wilby, R. L., Charles, S. P., Zorita, E., Timbal, B., Whetton, P., & Mearns, L. O.</p><p>(2004). Guidelines for Use of Climate Scenarios Developed from Statistical</p><p>Downscaling Methods. Analysis, 27(August), 127. https://doi.org/citeulikearticle-</p><p>id:8861447</p><p></p><p>Wiskott, L. (2016). Lecture notes on Principal Component Analysis.</p><p>https://doi.org/http://orcid.org/0000-0001-6237-740X</p><p></p><p>Wiskott, L., & Alberto N., E.-B. (2013). How to Solve Classification and Regression</p><p>Problems on High-Dimensional Data with a Supervised Extension of Slow</p><p>Feature Analysis. Journal of Machine Learning Research, 14, 36833719.</p><p>http://cogprints.org/8966/</p><p></p><p>Wong, T. T. (2015). Performance evaluation of classification algorithms by k-fold</p><p>and leave-one-out cross validation. Pattern Recognition, 48(9), 28392846.</p><p>https://doi.org/10.1016/j.patcog.2015.03.009</p><p></p><p>Wu, Y., & Liu, Y. (2007). Robust truncated hinge loss support vector machines.</p><p>Journal of the American Statistical Association, 102(479), 974983.</p><p>https://doi.org/10.1198/016214507000000617</p><p></p><p>Xia, Y. (2020). Correlation and association analyses in microbiome study integrating</p><p>multiomics in health and disease. In Progress in Molecular Biology and</p><p>Translational Science (1st ed., Vol. 171). Elsevier Inc.</p><p>https://doi.org/10.1016/bs.pmbts.2020.04.003</p><p></p><p>Xu, R., Chen, N., Chen, Y., & Chen, Z. (2020). Downscaling and Projection of Multi-</p><p>CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River</p><p>Basin. Advances in Meteorology, 2020. https://doi.org/10.1155/2020/8680436</p><p></p><p>Yadav, S., & Shukla, S. (2016). Analysis of k-Fold Cross-Validation over Hold-Out</p><p>Validation on Colossal Datasets for Quality Classification. Proceedings - 6th</p><p>International Advanced Computing Conference, IACC 2016, Cv, 7883.</p><p>https://doi.org/10.1109/IACC.2016.25</p><p></p><p>Ye, Y., Xiong, Y., Zhou, Q., Wu, J., Li, X., & Xiao, X. (2020). Comparison of</p><p>Machine Learning Methods and Conventional Logistic Regressions for</p><p>Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective</p><p>Cohort Study. Journal of Diabetes Research, 2020.</p><p>https://www.hindawi.com/journals/jdr/2020/4168340/</p><p></p><p>Zainudin, S., Jasim, D. S., & Bakar, A. A. (2016). Comparative analysis of data</p><p>mining techniques for malaysian rainfall prediction. International Journal on</p><p>Advanced Science, Engineering and Information Technology, 6(6), 11481153.</p><p>https://doi.org/10.18517/ijaseit.6.6.1487</p><p></p><p>Zhang, D., Tan, M. L., Dawood, S. R. S., Samat, N., Chang, C. K., Roy, R., Tew, Y.</p><p>L., & Mahamud, M. A. (2020). Comparison of ncep-cfsr and cmads for</p><p>hydrological modelling using swat in the muda river basin, malaysia. Water</p><p>(Switzerland), 12(11). https://doi.org/10.3390/w12113288</p><p></p><p>Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial neural</p><p>networks in bankruptcy prediction: general framework and cross-validation</p><p>analysis. European Journal of Operational Research, 116(1), 1632.</p><p>https://doi.org/10.1016/S0377-2217(98)00051-4</p><p></p><p>Zhang, Yongli. (2012). Support vector machine classification algorithm and its</p><p>application. Communications in Computer and Information Science, 308</p><p>CCIS(PART 2), 179186. https://doi.org/10.1007/978-3-642-34041-3_27</p><p></p><p>Zhang, Yudong, & Wu, L. (2012). Classification of fruits using computer vision and a</p><p>multiclass support vector machine. Sensors (Switzerland), 12(9), 1248912505.</p><p>https://doi.org/10.3390/s120912489</p><p></p><p>Zhao, Y., & Miner, S. D. (2014). Data Mining Applications with R: ProQuest Tech</p><p>Books.</p><p>http://proquest.safaribooksonline.com.proxy1.library.mcgill.ca/book/programmin</p><p>g/r/9780124115118</p><p></p><p>Zorita, E., & von Storch, H. (1997). A survey of statistical downscaling techniques.</p><p>GKSS Report, 20. https://www.osti.gov/etdeweb/servlets/purl/595191</p><p></p><p>Zoro, R. (2012). How to explain poor classification performance of recall when using</p><p>SVM. Cross Validated. https://stats.stackexchange.com/q/22208</p><p></p> |