A novel generic optimization method for irrigation scheduling under multiple objectives and multiple hierarchical layers in a canal network. (July 2017)

• Record Type: Journal Article
• Authors: Delgoda, Dilini; Malano, Hector; Saleem, Syed K.; Halgamuge, Malka N.
• Is Part Of: Advances in water resources. Volume 105(2017)
• Page Start: 188
• Page End: 204
• Publication Date: 2017-07
• Subjects: Multi-objective -- Optimization -- Canal network -- Multiple layers -- Genetic algorithms -- Pareto frontier; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2017.04.025
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: A generic approach to optimize canal scheduling in a multi-hierarchical canal network, under multiple conflicting objectives. A network-wide solution that is also optimal for lower hierarchical layers. Flexibility for the decision maker to change local priorities based on immediate network priorities. Solvable using available multi-objective evolutionary algorithms. Independent of mathematical modeling of objectives and process modeling of network hydraulics - Lets easy addition of details. Abstract: This research proposes a novel generic method for irrigation scheduling in a canal network to optimize multiple objectives related to canal scheduling (e.g. maximizing water supply and minimizing imbalance of water distribution) within multiple hierarchical layers (e.g. the layers consisting of the main canal, distributaries) while utilizing traditional canal scheduling methods. It is based on modularizing the optimization process. The method is theoretically capable of optimizing an unlimited number of user-defined objectives within an unlimited number of hierarchical layers and only limited by resource availability (e.g. maximum canal capacity and water limitations) in the network. It allows flexible decision-making through quantification of the mutual effects of optimizing conflicting objectives and is adaptable to available multi-objective evolutionary algorithms. The method's application is demonstrated using a hypothetical canal network example with six objectives and three hierarchical layers, and a real scenario with four objectives and two layers.