Application of the Whale Optimization Algorithm (WOA) in Reservoir Optimization Operation Under Investigation of Climate Change Impact: A Case Study at Klang Gate Dam, Malaysia

The effectiveness of analyzing large amounts of data that comes with engaging climate change scenarios, for planning advanced reservoir management can be achieved through the use of optimization algorithms. The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm derived following an...

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Main Authors: Lai V., Huang Y.F., Koo C.H., Ahmed A.N., El-Shafie A.
Other Authors: 57204919704
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2024
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spelling my.uniten.dspace-344992024-10-14T11:20:12Z Application of the Whale Optimization Algorithm (WOA) in Reservoir Optimization Operation Under Investigation of Climate Change Impact: A Case Study at Klang Gate Dam, Malaysia Lai V. Huang Y.F. Koo C.H. Ahmed A.N. El-Shafie A. 57204919704 55807263900 57204843657 57214837520 16068189400 Global circulation models (GCMs) Optimization Reservoir operation Climate change Climate models Digital storage Failure rate Neural networks Reservoir management Reservoirs (water) Climate change impact Failure rate Global circulation model Malaysia Optimisations Optimization algorithms Optimization operation Reservoir operation Reservoir optimizations Optimization The effectiveness of analyzing large amounts of data that comes with engaging climate change scenarios, for planning advanced reservoir management can be achieved through the use of optimization algorithms. The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm derived following animal-behaviour-based concepts. In Malaysia, specifically at the Klang Gate Dam (KGD), very little organized information has been collected in investigating future reservoir operations considering such climate anomalies and complexities. Hence, this study at the KGD is to assist policymakers in gaining a better knowledge of reservoir operations, and to determine the optimal water releases, during the projected future climate forecasts. The analysis begins with the maximum water temperature demand from 2020 to 2099, in which the data is obtained from the Coupled Model Intercomparison Project 5 (CMIP5) under RCP 2.6, RCP 4.5, and RCP 8.5 simulations, which were then applied in this study. In the simulation process, an artificial neural network (ANN) was used. The results were then compared to the WOA in terms of reservoir risk evaluation performance. During the optimization phase, the average storage failure rate for all the RCPs was 34.93%, while during the simulation phase, the average storage failure rate was 97.29%. In terms of managing reservoir operation and storage failure, the WOA performed substantially better (50% more robust than the simulation procedure). In terms of periodic reliability, the shortage periods under RCP 2.6 and RCP 8.5 yield 1.15 and 9.58%, respectively. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023. Final 2024-10-14T03:20:12Z 2024-10-14T03:20:12Z 2023 Conference Paper 10.1007/978-981-99-4101-8_7 2-s2.0-85177822557 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177822557&doi=10.1007%2f978-981-99-4101-8_7&partnerID=40&md5=df1036bf79b8ab7eff2157da02af2214 https://irepository.uniten.edu.my/handle/123456789/34499 93 103 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Global circulation models (GCMs)
Optimization
Reservoir operation
Climate change
Climate models
Digital storage
Failure rate
Neural networks
Reservoir management
Reservoirs (water)
Climate change impact
Failure rate
Global circulation model
Malaysia
Optimisations
Optimization algorithms
Optimization operation
Reservoir operation
Reservoir optimizations
Optimization
spellingShingle Global circulation models (GCMs)
Optimization
Reservoir operation
Climate change
Climate models
Digital storage
Failure rate
Neural networks
Reservoir management
Reservoirs (water)
Climate change impact
Failure rate
Global circulation model
Malaysia
Optimisations
Optimization algorithms
Optimization operation
Reservoir operation
Reservoir optimizations
Optimization
Lai V.
Huang Y.F.
Koo C.H.
Ahmed A.N.
El-Shafie A.
Application of the Whale Optimization Algorithm (WOA) in Reservoir Optimization Operation Under Investigation of Climate Change Impact: A Case Study at Klang Gate Dam, Malaysia
description The effectiveness of analyzing large amounts of data that comes with engaging climate change scenarios, for planning advanced reservoir management can be achieved through the use of optimization algorithms. The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm derived following animal-behaviour-based concepts. In Malaysia, specifically at the Klang Gate Dam (KGD), very little organized information has been collected in investigating future reservoir operations considering such climate anomalies and complexities. Hence, this study at the KGD is to assist policymakers in gaining a better knowledge of reservoir operations, and to determine the optimal water releases, during the projected future climate forecasts. The analysis begins with the maximum water temperature demand from 2020 to 2099, in which the data is obtained from the Coupled Model Intercomparison Project 5 (CMIP5) under RCP 2.6, RCP 4.5, and RCP 8.5 simulations, which were then applied in this study. In the simulation process, an artificial neural network (ANN) was used. The results were then compared to the WOA in terms of reservoir risk evaluation performance. During the optimization phase, the average storage failure rate for all the RCPs was 34.93%, while during the simulation phase, the average storage failure rate was 97.29%. In terms of managing reservoir operation and storage failure, the WOA performed substantially better (50% more robust than the simulation procedure). In terms of periodic reliability, the shortage periods under RCP 2.6 and RCP 8.5 yield 1.15 and 9.58%, respectively. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.
author2 57204919704
author_facet 57204919704
Lai V.
Huang Y.F.
Koo C.H.
Ahmed A.N.
El-Shafie A.
format Conference Paper
author Lai V.
Huang Y.F.
Koo C.H.
Ahmed A.N.
El-Shafie A.
author_sort Lai V.
title Application of the Whale Optimization Algorithm (WOA) in Reservoir Optimization Operation Under Investigation of Climate Change Impact: A Case Study at Klang Gate Dam, Malaysia
title_short Application of the Whale Optimization Algorithm (WOA) in Reservoir Optimization Operation Under Investigation of Climate Change Impact: A Case Study at Klang Gate Dam, Malaysia
title_full Application of the Whale Optimization Algorithm (WOA) in Reservoir Optimization Operation Under Investigation of Climate Change Impact: A Case Study at Klang Gate Dam, Malaysia
title_fullStr Application of the Whale Optimization Algorithm (WOA) in Reservoir Optimization Operation Under Investigation of Climate Change Impact: A Case Study at Klang Gate Dam, Malaysia
title_full_unstemmed Application of the Whale Optimization Algorithm (WOA) in Reservoir Optimization Operation Under Investigation of Climate Change Impact: A Case Study at Klang Gate Dam, Malaysia
title_sort application of the whale optimization algorithm (woa) in reservoir optimization operation under investigation of climate change impact: a case study at klang gate dam, malaysia
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2024
_version_ 1814061059287285760
score 13.209306