Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting
Flood, which is the most common natural disaster that occurs worldwide, causes massive casualties and damages to people and environment respectively. Hence, flood prediction is integral to minimise the damage and loss of life, while simultaneously aiding the government authorities and even the pr...
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| 格式: | Thesis |
| 語言: | English English English |
| 出版: |
2021
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| 主題: | |
| 在線閱讀: | http://eprints.uthm.edu.my/6319/1/24p%20LOH%20ENG%20CHUEN.pdf http://eprints.uthm.edu.my/6319/2/LOH%20ENG%20CHUEN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6319/3/LOH%20ENG%20CHUEN%20WATERMARK.pdf |
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| 總結: | Flood, which is the most common natural disaster that occurs worldwide, causes
massive casualties and damages to people and environment respectively. Hence, flood
prediction is integral to minimise the damage and loss of life, while simultaneously
aiding the government authorities and even the private sector in making accurate
decisions when faced with incoming flood. Therefore, this present study had imputed
the missing hydrological data using five imputation methods, namely Neural Network
(NN), Moving Median (MM), Iterative Algorithm (IA), Nonlinear Iterative Partial
Least Square (NIPALS), and Combined Correlation with Inversed Distance (CCID)
imputation methods. Next, a newly developed hybrid deep learning (DL) algorithm is
proposed to predict the daily water level in selected rivers that flow through Kelantan.
The proposed model was then compared with two benchmark models, namely single
Artificial Neural Network (ANN) and Wavelet Artificial Neural Network (WANN).
The outcomes revealed that the MM imputation method resulted in higher accuracy
with the lowest Root Mean Square Error (RMSE) for all rainfall and streamflow
stations, in comparison to the other imputation methods. The experimental results
portrayed that the proposed model achieved the best prediction accuracy in all
performance measurements. The Mean Arctangent Absolute Percentage Error
(MAAPE) results for all rivers ranged at 1-12%, which signified higher accuracy.
Essentially, the proposed model may facilitate the government authorities and private
sector to predict and plan better when dealing with the occurrence of flood. |
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