Solving multi-objective water management problems using evolutionary computation. (15th December 2017)

• Record Type: Journal Article
• Authors: Lewis, A.; Randall, M.
• Is Part Of: Journal of environmental management. Volume 204:Part 1(2017)
• Page Start: 179
• Page End: 188
• Publication Date: 2017-12-15
• Subjects: Water resource management -- Multi-objective optimisation -- Evolutionary computation -- Non-dominated sorting genetic algorithm-II -- Pareto optimisation; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2017.08.044
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: 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. Highlights: Contemporary model for crop selection with environmental considerations developed. Multi-objective solver used to produce realistic sets of trade-off solutions. This work provides for future development of integrated water planning tools.