Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm

The Department of Irrigation and Drainage (DID) and Meteorological Malaysia Department (MMD) have identified that water level is one of the important indicators for flooding control. The aim of this study is to find the best regression model and to identify the dominant variables of water level in D...

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主要作者:	Arbain, Siti Hajar
格式:	Thesis
語言:	English
出版:	2014
主題:	QA75 Electronic computers Computer science
在線閱讀:	http://eprints.utm.my/id/eprint/50690/25/SitiHajarArbainMFC2014.pdf
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id	my-utm-ep.50690
record_format	uketd_dc
spelling	my-utm-ep.506902020-07-08T04:17:25Z Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm 2014-09 Arbain, Siti Hajar QA75 Electronic computers. Computer science The Department of Irrigation and Drainage (DID) and Meteorological Malaysia Department (MMD) have identified that water level is one of the important indicators for flooding control. The aim of this study is to find the best regression model and to identify the dominant variables of water level in Dungun River. Autoregressive Integrated Moving Average (ARIMA),Seasonal ARIMA (SARIMA), Backpropagation Neural Network (BPNN) and Nonlinear Autoregressive Exogenous Model (NARX) are popular methods in time series forecasting. However, ARIMA and SARIMA produce linear models where the approximations of linear models for the complex real-world problems are not always satisfactory. Thus, Backpropagation Neural Network (BPNN) and Nonlinear Autoregressive Exogenous Model (NARX) can be implemented in the time series forescasting due to its nonlinear modelling capability. These four methods, however, cannot be used directly for water level prediction since the original data from DID and MMD contain missing data. In this thesis, two methods are employed to treat missing data which are pre-processing using Mean and preprocessing using Ordinary Linear Regression (OLR) substitutions. In addition, BPNN and NARX may be difficult to determine the optimal network architecture and weights design since the optimal weight are different in each learning process. Thus, it is difficult to get best model in prediction. Based on the limitation of BPNN and NARX, the hybridization of Single BPPN and Genetic Algorithms (S-BPNN-GA) and Multi BPNN and Genetic Algorithms (M-BPNN-GA) have been proposed in this study. Experiments indicate hybridization of M-BPNN-GA 5-6-1 using five predictor variables including monthly, rainfall, temperature, evaporation and humidity and give better results compared to the other methods. 2014-09 Thesis http://eprints.utm.my/id/eprint/50690/ http://eprints.utm.my/id/eprint/50690/25/SitiHajarArbainMFC2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:92578 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Arbain, Siti Hajar Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm
description	The Department of Irrigation and Drainage (DID) and Meteorological Malaysia Department (MMD) have identified that water level is one of the important indicators for flooding control. The aim of this study is to find the best regression model and to identify the dominant variables of water level in Dungun River. Autoregressive Integrated Moving Average (ARIMA),Seasonal ARIMA (SARIMA), Backpropagation Neural Network (BPNN) and Nonlinear Autoregressive Exogenous Model (NARX) are popular methods in time series forecasting. However, ARIMA and SARIMA produce linear models where the approximations of linear models for the complex real-world problems are not always satisfactory. Thus, Backpropagation Neural Network (BPNN) and Nonlinear Autoregressive Exogenous Model (NARX) can be implemented in the time series forescasting due to its nonlinear modelling capability. These four methods, however, cannot be used directly for water level prediction since the original data from DID and MMD contain missing data. In this thesis, two methods are employed to treat missing data which are pre-processing using Mean and preprocessing using Ordinary Linear Regression (OLR) substitutions. In addition, BPNN and NARX may be difficult to determine the optimal network architecture and weights design since the optimal weight are different in each learning process. Thus, it is difficult to get best model in prediction. Based on the limitation of BPNN and NARX, the hybridization of Single BPPN and Genetic Algorithms (S-BPNN-GA) and Multi BPNN and Genetic Algorithms (M-BPNN-GA) have been proposed in this study. Experiments indicate hybridization of M-BPNN-GA 5-6-1 using five predictor variables including monthly, rainfall, temperature, evaporation and humidity and give better results compared to the other methods.
format	Thesis
qualification_level	Master's degree
author	Arbain, Siti Hajar
author_facet	Arbain, Siti Hajar
author_sort	Arbain, Siti Hajar
title	Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm
title_short	Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm
title_full	Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm
title_fullStr	Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm
title_full_unstemmed	Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm
title_sort	non-linear water level forecasting of dungun river using hybridization of backpropagation neural network and genetic algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/50690/25/SitiHajarArbainMFC2014.pdf
_version_	1747817512661155840

Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm

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