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CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION

It is more crucial than ever to make quantitative prediction patterns of precipitation due to climate change and global warming concerns. The foundation for many climate change simulations is global circulation models (GCMs). However, to create finer models for regional use, researchers have been em...

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Main Authors: Kuok, King Kuok, Chiu, Po Chan, Md. Rezaur, Rahman, Khairul Anwar, Mohamad Said
Format: Book Chapter
Language:English
Published: Cambridge Scholars Publishing 2024
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/46908/1/Cuckoo%20Search.pdf
http://ir.unimas.my/id/eprint/46908/
https://www.cambridgescholars.com/product/978-1-0364-0804-6
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spelling my.unimas.ir-469082024-12-24T03:49:27Z http://ir.unimas.my/id/eprint/46908/ CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION Kuok, King Kuok Chiu, Po Chan Md. Rezaur, Rahman Khairul Anwar, Mohamad Said T Technology (General) It is more crucial than ever to make quantitative prediction patterns of precipitation due to climate change and global warming concerns. The foundation for many climate change simulations is global circulation models (GCMs). However, to create finer models for regional use, researchers have been employing various downscaling strategies due to their coarse resolution. Technological developments in metaheuristic algorithms have introduced a different method for downscaling. This paper presents the application of a novel optimization algorithm, Cuckoo Search Optimization (CSO), to train feedforward neural networks to forecast long-term precipitation using three climate models, namely HadCM3, ECHAM5, and HadGEM3‐RA. The selected study area is Kuching City, Sarawak, Malaysia, and the models' performance was assessed using historical precipitation data validation through the square root of the correlation of determination (r), mean absolute error (MAE), root mean square error (RMSE), and Nash and Sutcliffe coefficient (E). With a setup of 20 nests (n), an initial alien egg-finding rate (Pa) of 0.6, 100 hidden neurons (HN), 1000 iterations (IN), and a learning rate (LR) of 1, the results demonstrated that the Cuckoo Search Optimization Neural Network (CSONN) is capable of forecasting precipitation with confidence levels of 95%~99% for r and 85%~94% for E, alongside lower RMSE and MAE. Future precipitation forecasts revealed that the city would experience an increase in mean monthly precipitation of 2%~26% in the 2030s, 0%~34% in the 2050s, and 4%~43% in the 2080s during wet seasons, relative to the 1970s. The findings also showed that mean monthly precipitation would decrease during dry seasons, ranging from 1%~4% in the 2030s, 1%~2% in the 2050s, and 3%~4% in the 2080s, compared to the 1970s. Cambridge Scholars Publishing Kuok, King Kuok Rezaur, Rahman 2024-08-30 Book Chapter PeerReviewed text en http://ir.unimas.my/id/eprint/46908/1/Cuckoo%20Search.pdf Kuok, King Kuok and Chiu, Po Chan and Md. Rezaur, Rahman and Khairul Anwar, Mohamad Said (2024) CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION. In: Metaheuristic Algorithms and Neural Networks in Hyd. Cambridge Scholars Publishing, pp. 83-104. 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
Khairul Anwar, Mohamad Said
CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION
description It is more crucial than ever to make quantitative prediction patterns of precipitation due to climate change and global warming concerns. The foundation for many climate change simulations is global circulation models (GCMs). However, to create finer models for regional use, researchers have been employing various downscaling strategies due to their coarse resolution. Technological developments in metaheuristic algorithms have introduced a different method for downscaling. This paper presents the application of a novel optimization algorithm, Cuckoo Search Optimization (CSO), to train feedforward neural networks to forecast long-term precipitation using three climate models, namely HadCM3, ECHAM5, and HadGEM3‐RA. The selected study area is Kuching City, Sarawak, Malaysia, and the models' performance was assessed using historical precipitation data validation through the square root of the correlation of determination (r), mean absolute error (MAE), root mean square error (RMSE), and Nash and Sutcliffe coefficient (E). With a setup of 20 nests (n), an initial alien egg-finding rate (Pa) of 0.6, 100 hidden neurons (HN), 1000 iterations (IN), and a learning rate (LR) of 1, the results demonstrated that the Cuckoo Search Optimization Neural Network (CSONN) is capable of forecasting precipitation with confidence levels of 95%~99% for r and 85%~94% for E, alongside lower RMSE and MAE. Future precipitation forecasts revealed that the city would experience an increase in mean monthly precipitation of 2%~26% in the 2030s, 0%~34% in the 2050s, and 4%~43% in the 2080s during wet seasons, relative to the 1970s. The findings also showed that mean monthly precipitation would decrease during dry seasons, ranging from 1%~4% in the 2030s, 1%~2% in the 2050s, and 3%~4% in the 2080s, compared to the 1970s.
author2 Kuok, King Kuok
author_facet Kuok, King Kuok
Kuok, King Kuok
Chiu, Po Chan
Md. Rezaur, Rahman
Khairul Anwar, Mohamad Said
format Book Chapter
author Kuok, King Kuok
Chiu, Po Chan
Md. Rezaur, Rahman
Khairul Anwar, Mohamad Said
author_sort Kuok, King Kuok
title CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION
title_short CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION
title_full CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION
title_fullStr CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION
title_full_unstemmed CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION
title_sort cuckoo search optimization neural network models for forecasting long-term precipitation
publisher Cambridge Scholars Publishing
publishDate 2024
url http://ir.unimas.my/id/eprint/46908/1/Cuckoo%20Search.pdf
http://ir.unimas.my/id/eprint/46908/
https://www.cambridgescholars.com/product/978-1-0364-0804-6
_version_ 1819914972283535360
score 13.223943

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