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  • Title: An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. Part I. Model development.
    Author: Wang X, Sun Y, Song L, Mei C.
    Journal: J Environ Manage; 2009 Jun; 90(8):2628-35. PubMed ID: 19269735.
    Abstract:
    We propose here an improved multi-objective optimisation model that considers eco-environmental water demand (EWD) for allocating water resources in a river basin over the long term. The model considers economic, social, and environmental objectives, and it improves on traditional optimisation methods by emphasizing not only the water demand of the artificial ecosystem but also that of the natural ecosystem. Water resource constraints are considered. The hybrid genetic simulated annealing algorithms (HGSAA) technique incorporates a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which have strong local and global searching abilities, in order to solve the highly non-linear model and avoid local and pre-mature convergence. In the method, the water demands of users in the planning year serve as the basis for long-term optimisation using a forecasting procedure. In this study, the combined forecasting method based on the principle of optimal combination is built to forecast domestic and industrial water demands. The proposed model and method are subsequently used in a companion paper to optimise water allocation in the Haihe River basin in China [An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. Part II. Model application and results 90 (8), 2612-2619].
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