Application of simulated annealing (SA) techniques for optimal water distribution in irrigation canals

Abstract

One of the contributing factors to the poor performance of irrigation systems is improper water distribution. Traditionally, in irrigation canals water distribution schedules are determined based on descriptive methods. Application of optimization methods could improve performance of water delivery. In a case study, a singleobjective zero-one programming method was used to develop an optimal water delivery schedule. Considering several objectives of irrigation systems and the difficulties of zero-one programming, a multi-objective simulated annealing optimization technique is utilized. Based on water requirements at delivery points, the developed model determines time and rate of water delivery. The canal capacity and operational stages are minimized and water requirements are satisfied as closely as possible. In order to test the applicability of the proposed model, the model is applied on the Feng-Jia-Shan Canal in China and compared with the results of Wang. The results show that the multi-objective optimization approach resulted in lower operational stages, while the canal capacity is reduced to the same level as Wang's study. The model is applied on the AMX canal of the Varamin irrigation network in Tehran, Iran. The model benefits from the advantages of SA techniques and could take into account several objectives and avoid the difficulties of zero-one programming. This model could be used as an efficient tool for planning water delivery schedules in irrigation canals.

Pipe network system analysis using simulated annealing

“…In addition, Romeo & Sangiovanni-Vincentelli (1991) also used homogeneous Markov chain and inhomogeneous Markov chain theory to prove that SA can converge to globally optimal solutions. SA was successfully applied in a wide range of optimization applications such as capacity extension for pipe network system (Cunha & Sousa 1999;Monem & Namdarian 2005), parameter calibration and identification problems (Huang & Yeh 2007;Yeh & Chen 2007;, groundwater management problems (Doutherty & Marryott 1991;Marryott et al 1993), groundwater remediation system problems (Marryott 1996;Rizzo & Dougherty 1996;Shieh & Peralta 2005), etc. Cunha & Sousa (1999) used SA to minimize the capacity extension cost in a water distribution network.…”

Planning regional wastewater systems across borders

“…Modern heuristics are often inspired in natural processes to apply search strategies that can avoid local optima and have become very popular among scientists and engineers to handle such models [3]. In particular, the simulated annealing (SA) algorithm has been used with remarkable results in several hydraulic system planning models [4,5]. Recently, Cunha et al [6] described a realistic discrete nonlinear optimization model for regional wastewater system planning solved through an SA algorithm.…”

An Efficient Simulated Annealing Algorithm for Regional Wastewater System Planning

“…Since the 1980s, numerous heuristic algorithms (e.g., genetic algorithms, tabu search, neural networks, and simulated annealing) have been successfully developed to determine optimum (or near-optimum) solutions to complex civil engineering models (Adeli and Cheng, 1994;Savic and Walters, 1997;Jiang and Adeli, 2008). In particular, simulated annealing (SA) algorithms have been applied with remarkable results to several hydraulic system planning models (Cunha and Sousa, 1999;Dougherty and Marryott, 1991;McCormick and Powell, 2004;Monem and Namdarian, 2005).…”