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WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE

The influence of climate change is crucial to ensure effective planning and management of water resources in the future. Researchers previously adopted conventional artificial neural network (ANN) models for solving optimization problems. However, conventional ANN models exhibited a tendency for loc...

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Main Authors: Kuok, King Kuok, Chiu, Po Chan, Md. Rezaur, Rahman, Teng, Yeow Haur
Other Authors: King Kuok, Kuok
Format: Book Chapter
Language:English
Published: Cambridge Scholars Publishing 2024
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/46909/1/Whale%20Optimization.pdf
http://ir.unimas.my/id/eprint/46909/
https://www.cambridgescholars.com/product/978-1-0364-0804-6
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spelling my.unimas.ir-469092024-12-24T03:55:02Z http://ir.unimas.my/id/eprint/46909/ WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE Kuok, King Kuok Chiu, Po Chan Md. Rezaur, Rahman Teng, Yeow Haur T Technology (General) The influence of climate change is crucial to ensure effective planning and management of water resources in the future. Researchers previously adopted conventional artificial neural network (ANN) models for solving optimization problems. However, conventional ANN models exhibited a tendency for local optima trapping, rendering them ineffective as the complexity of optimization problems increased. This issue can be addressed using metaheuristic-based ANN models, such as Whale Optimization Neural Networks (WONN), for developing a water level model at the Batu Kitang Submersible Weir (BKSW). Hyper-parameter tuning was conducted to determine the optimal configuration of WONN using the GFDL-CM3 Global Circulation Model (GCM) under the RCP4.5 scenario. A total of 2,555 daily data points were used, with 70% allocated for training and 30% for testing. The models' performance was evaluated based on average mean absolute error, average root mean square error, and average correlation coefficient. Results revealed that the optimal configuration of WONN was determined to be 18 hidden nodes, 30 search agents, 250 maximum iterations, and 2,500 epochs, yielding an average mean absolute error of 0.1425, an average root mean square error of 0.1989, and an average correlation coefficient of 0.9097. The future long-term daily weir water level is forecasted to increase over the years, necessitating further efforts and measures to control water downstream for flood mitigation purposes. Cambridge Scholars Publishing King Kuok, Kuok Rezaur, Rahman 2024-08-30 Book Chapter PeerReviewed text en http://ir.unimas.my/id/eprint/46909/1/Whale%20Optimization.pdf Kuok, King Kuok and Chiu, Po Chan and Md. Rezaur, Rahman and Teng, Yeow Haur (2024) WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE. In: Metaheuristic Algorithms and Neural Networks in Hydrology. Cambridge Scholars Publishing, pp. 105-129. ISBN 978-1-0364-0804-6 https://www.cambridgescholars.com/product/978-1-0364-0804-6
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Kuok, King Kuok
Chiu, Po Chan
Md. Rezaur, Rahman
Teng, Yeow Haur
WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE
description The influence of climate change is crucial to ensure effective planning and management of water resources in the future. Researchers previously adopted conventional artificial neural network (ANN) models for solving optimization problems. However, conventional ANN models exhibited a tendency for local optima trapping, rendering them ineffective as the complexity of optimization problems increased. This issue can be addressed using metaheuristic-based ANN models, such as Whale Optimization Neural Networks (WONN), for developing a water level model at the Batu Kitang Submersible Weir (BKSW). Hyper-parameter tuning was conducted to determine the optimal configuration of WONN using the GFDL-CM3 Global Circulation Model (GCM) under the RCP4.5 scenario. A total of 2,555 daily data points were used, with 70% allocated for training and 30% for testing. The models' performance was evaluated based on average mean absolute error, average root mean square error, and average correlation coefficient. Results revealed that the optimal configuration of WONN was determined to be 18 hidden nodes, 30 search agents, 250 maximum iterations, and 2,500 epochs, yielding an average mean absolute error of 0.1425, an average root mean square error of 0.1989, and an average correlation coefficient of 0.9097. The future long-term daily weir water level is forecasted to increase over the years, necessitating further efforts and measures to control water downstream for flood mitigation purposes.
author2 King Kuok, Kuok
author_facet King Kuok, Kuok
Kuok, King Kuok
Chiu, Po Chan
Md. Rezaur, Rahman
Teng, Yeow Haur
format Book Chapter
author Kuok, King Kuok
Chiu, Po Chan
Md. Rezaur, Rahman
Teng, Yeow Haur
author_sort Kuok, King Kuok
title WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE
title_short WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE
title_full WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE
title_fullStr WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE
title_full_unstemmed WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE
title_sort whale optimization neural network for daily water level forecasting considering the changing climate
publisher Cambridge Scholars Publishing
publishDate 2024
url http://ir.unimas.my/id/eprint/46909/1/Whale%20Optimization.pdf
http://ir.unimas.my/id/eprint/46909/
https://www.cambridgescholars.com/product/978-1-0364-0804-6
_version_ 1819914972426141696
score 13.223943

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