Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/14339
Title: Optimization of multi-objective reservoir operation system
Authors: TARIQ ABDULLAH AL-FAYYAZ
Keywords: Multiobjective optimization; Reservoir operation; Non-dominated Sorting GA; Pareto-optimal solutions; Neural Network; Mike Basin.
Issue Date: 24-Nov-2004
Source: TARIQ ABDULLAH AL-FAYYAZ (2004-11-24). Optimization of multi-objective reservoir operation system. ScholarBank@NUS Repository.
Abstract: Optimization in planning, design and implementation of complex water resources systems have always been an intensive research activities. Reservoir is one of the major storages of surface water and optimally operating single or multi-reservoir network system forms an integral part of water resources management. A multi-objective evolutionary algorithm (MOEA) based innovative approach namely; Elitist Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) by Deb et al. (2000) is applied in this study due to its reported effectiveness and efficiency. A novel clustering algorithm on the Pareto surface (Sivapragasam; 2002) to aid the reservoir operator in efficient decision making is demonstrated on a real large-scale multi-objective reservoir system in Chaliyar River Basin in India and on a hypothetical inter-connected reservoir. In this study, the application of Mike Basin equipped with ArcView GIS, is demonstrated on a multi-objective inter-connected reservoir operation system. Mike Basin is user friendly to the reservoir operator (decision maker) offering a series of preferred solutions on GUI of ArcView GIS and other usual time series format.
URI: http://scholarbank.nus.edu.sg/handle/10635/14339
Appears in Collections:Master's Theses (Open)

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