Solving multi-objective water management problems using evolutionary computation

Andrew Lewis

doi:10.1016/j.jenvman.2017.08.044

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Solving multi-objective water management problems using evolutionary computation

2017-12-01

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Andrew Lewis *

Marcus Randall *

DOI：10.1016/j.jenvman.2017.08.044

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Abstract

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Water as a resource is becoming increasingly more valuable given the changes in global climate. In an agricultural sense, the role of water is vital to ensuring food security. Therefore the management of it has become a subject of increasing attention and the development of effective tools to support participative decision-making in water management will be a valuable contribution. In this paper, evolutionary computation techniques and Pareto optimisation are incorporated in a model-based system for water management. An illustrative test case modelling optimal crop selection across dry, average and wet years based on data from the Murrumbidgee Irrigation Area in Australia is presented. It is shown that sets of trade-off solutions that provide large net revenues, or minimise environmental flow deficits can be produced rapidly, easily and automatically. The system is capable of providing detailed information on optimal solutions to achieve desired outcomes, responding to a variety of factors including climate conditions and economics. (C) 2017 Elsevier Ltd. All rights reserved.

Keywords:

Water resource management

Multi-objective optimisation

Evolutionary computation

Non-dominated sorting genetic algorithm-II

Pareto optimisation

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