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. (Springer Science and Business Media Deutschland GmbH, 2024)
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.