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Open AccessArticle

Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

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GIS Division, Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
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GIS Division, Faculty of Geodesy and Geomatics, and Geoinformation Technology Center of Excellence, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
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Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia
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Department of Civil Engineering, Indian Institute of Technology Indore (IITI), Khandwa Road, Simrol, Indore 453552, India
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Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
*
Author to whom correspondence should be addressed.
Water 2019, 11(12), 2611; https://doi.org/10.3390/w11122611
Received: 23 October 2019 / Revised: 1 December 2019 / Accepted: 6 December 2019 / Published: 11 December 2019
(This article belongs to the Section Water Resources Management, Policy and Governance)
The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method. View Full-Text
Keywords: agriculture; irrigation; water allocation; optimisation; meta-heuristic algorithm; GIS agriculture; irrigation; water allocation; optimisation; meta-heuristic algorithm; GIS
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Saeidian, B.; Mesgari, M.S.; Pradhan, B.; Alamri, A.M. Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms. Water 2019, 11, 2611.

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