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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Main Author:	Loh, Eng Chuen
Format:	Thesis
Language:	English English English
Published:	2021
Subjects:	GB Physical geography
Online Access:	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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id	my-uthm-ep.6319
record_format	uketd_dc
spelling	my-uthm-ep.63192022-02-05T07:21:38Z Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting 2021-05 Loh, Eng Chuen GB Physical geography 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. 2021-05 Thesis http://eprints.uthm.edu.my/6319/ http://eprints.uthm.edu.my/6319/1/24p%20LOH%20ENG%20CHUEN.pdf text en public http://eprints.uthm.edu.my/6319/2/LOH%20ENG%20CHUEN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/6319/3/LOH%20ENG%20CHUEN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Malaysia Fakulti Sains Gunaan dan Teknologi
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	GB Physical geography
spellingShingle	GB Physical geography Loh, Eng Chuen Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting
description	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.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Loh, Eng Chuen
author_facet	Loh, Eng Chuen
author_sort	Loh, Eng Chuen
title	Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting
title_short	Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting
title_full	Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting
title_fullStr	Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting
title_full_unstemmed	Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting
title_sort	kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Sains Gunaan dan Teknologi
publishDate	2021
url	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
_version_	1747831061523464192

Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting

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