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In interconnected urban water systems, the major issue raised during the planning and management phase such as rapid urbanization, climate variability, and growing environmental pressures. To overcome this research issues, proposed the Hybrid Adaptive Spatio-Temporal Evolutionary Multi-Objective Optimization (HAST-EMO) framework for robust urban water network management. This proposed approach integrates the spatio temporal method, NSGA-II-based optimization, and TOPSIS decision support later for the better urban water network management. The existing multi-objective optimization approaches struggles to handle the various dynamic water infrastructure environment. But the proposed framework provides an efficient deployment and time dependent framework for the water management systems. Simulation results show that the proposed framework provides better performance in pressure violation (60%), energy consumption (18%), faster convergence (38%), and flood volume (31%).

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Hybrid Spatio-Temporal NSGA-II–TOPSIS Optimization Framework for Intelligent Urban Water Network Management

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  • 发表日期：

    2026年

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  • J. Vijitha Ananthi,
  • James Deva Koresh Hezekiah

摘要

In interconnected urban water systems, the major issue raised during the planning and management phase such as rapid urbanization, climate variability, and growing environmental pressures. To overcome this research issues, proposed the Hybrid Adaptive Spatio-Temporal Evolutionary Multi-Objective Optimization (HAST-EMO) framework for robust urban water network management. This proposed approach integrates the spatio temporal method, NSGA-II-based optimization, and TOPSIS decision support later for the better urban water network management. The existing multi-objective optimization approaches struggles to handle the various dynamic water infrastructure environment. But the proposed framework provides an efficient deployment and time dependent framework for the water management systems. Simulation results show that the proposed framework provides better performance in pressure violation (60%), energy consumption (18%), faster convergence (38%), and flood volume (31%).