Assessment of water resources management strategy under different evolutionary optimization techniques

Jafar Y. Al-Jawad, Robert M. Kalin

Research output: Contribution to journalArticle

Abstract

Competitive optimization techniques have been developed to address the complexity of integrated water resources management (IWRM) modelling; however, model adaptation due to changing environments is still a challenge. In this paper we employ multi-variable techniques to increase confidence in model-driven decision-making scenarios. Here, water reservoir management was assessed using two evolutionary algorithm (EA) techniques, the epsilon-dominance-driven self-adaptive evolutionary algorithm (∈-DSEA) and the Borg multi-objective evolutionary algorithm (MOEA). Many objective scenarios were evaluated to manage flood risk, hydropower generation, water supply, and release sequences over three decades. Computationally, the ∈-DSEA's results are generally reliable, robust, effective and efficient when compared directly with the Borg MOEA but both provide decision support model outputs of value.

LanguageEnglish
Article number2021
Number of pages23
JournalWater
Volume11
Issue number10
DOIs
Publication statusPublished - 28 Sep 2019

Fingerprint

Water Resources
Water resources
water management
Evolutionary algorithms
water
management
resources
scenario
Reservoir management
water reservoirs
water power
Decision Support Techniques
Water Supply
hydropower
decision support systems
Adaptive algorithms
methodology
Water supply
water supply
Decision Making

Keywords

  • self-adaptive technique
  • many-objective
  • multi-variable
  • reservoir operation strategy

Cite this

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Assessment of water resources management strategy under different evolutionary optimization techniques. / Al-Jawad, Jafar Y.; Kalin, Robert M.

Vol. 11, No. 10, 2021, 28.09.2019.

Research output: Contribution to journalArticle

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AU - Kalin, Robert M.

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KW - many-objective

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