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Risk assessment for optimal drought management of an integrated water resources system using a genetic algorithm
Author(s) -
Merabtene Tarek,
Kawamura Akira,
Jinno Kenji,
Olsson Jonas
Publication year - 2002
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.1150
Subject(s) - genetic algorithm , computer science , vulnerability (computing) , water resources , reliability (semiconductor) , water supply , function (biology) , risk management , convergence (economics) , decision support system , mathematical optimization , index (typography) , risk analysis (engineering) , operations research , reliability engineering , data mining , environmental science , engineering , mathematics , business , machine learning , environmental engineering , economics , computer security , economic growth , ecology , world wide web , biology , power (physics) , quantum mechanics , evolutionary biology , physics , finance
A decision support system (DSS) is developed and applied to assess the susceptibility of water supply systems to droughts, and to aid decision‐makers in determining optimal supply strategies. The DSS integrates three fundamental modules for water resources management: (1) a real time rainfall‐runoff forecasting model enhanced by Kalman filtering; (2) a water demand forecast model; and (3) a reservoir operation model. Simulation and optimization procedures for the reservoir operation model are based on risk analysis to evaluate the system performance and to derive the most appropriate supply strategy of minimum risk, for the designed operating conditions. The optimization technique, based on genetic algorithms, introduces two new and distinct features, with the aim of minimizing the risks of drought damage and improving the convergence of the model toward practical solutions. Firstly, risk‐based measures of system performance, termed reliability, resiliency and vulnerability, are combined into a global risk index, referred to as the drought risk index (DRI). The DRI, formulated as a weighted function of the risk measures, serves as the objective function to be minimized during the search for the optimal operation. Secondly, in the genetic algorithm search, each new generation of water supply solutions is created from solutions with risk levels clustered inside a defined ‘acceptable risk space’. In other words, the convergence of the algorithm is improved by retaining only those solutions with DRI values smaller than the maximum acceptable risk. As a case study, the DSS is applied to the water resources system in Fukuoka City, western Japan. The DSS is believed to be an efficient tool for the assessment of a sequence of water supply scenarios, leading to the improved utilization of existing water resources during drought. Copyright © 2002 John Wiley & Sons, Ltd.