Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia.
Institute of Energy Infrastructure and Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia.
Sci Rep. 2023 Apr 28;13(1):6966. doi: 10.1038/s41598-023-33801-z.
To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09.
为缓解水资源短缺问题，人们已经应用动态规划、随机动态规划和启发式算法来解决与水资源相关的问题。在水库运行政策中，开发、运营和管理至关重要，尤其是当水库服务于复杂目标时。在这项研究中，尝试通过元启发式算法，即哈里斯鹰优化（HHO）算法和 HHO 的基于对立面的学习（OBL-HHO），来最小化 Klang Gate 大坝（KGD）下游的水亏缺和减轻洪灾。由于供水和洪水管理之间存在权衡，HHO 和 OBL-HHO 模型具有可配置的阈值来优化 KGD 水库运行。为了确定 HHO 和 OBL-HHO 在水库优化中的效果，评估了可靠性、脆弱性和弹性等风险指标。如果忽略流入类别，OBL-HHO 满足需求的比例为 71.49%，而独立的 HHO 为 54.83%。在中等流入量下，HHO 比 OBL-HHO 更能满足需求，达到 38.60%，而 OBL-HHO 仅为 20.61%，尽管 HHO 可能在存储水平结束时经历了水损失。与其他已发布的启发式算法相比，HHO 的可靠性和弹性指数分别为 62.50%和 1.56%，证明了它仍然是一种很有前途的方法。人工蜂群（ABC）的结果满足需求的比例为 61.36%，粒子群优化（PSO）为 59.47%，实数编码遗传算法（GA）为 55.68%，二进制 GA 为 23.5%。对于弹性，ABC 得分为 0.16，PSO 得分为 0.15，实数编码 GA 得分为 0.14，而二进制-GA 具有最差的故障恢复算法，得分为 0.09。
