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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/106017


    Title: AI techniques for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands
    Authors: Wen-Ping Tsai;Fi-John Chang;Li-Chiu Chang;Edwin E. Herricks
    Keywords: Artificial intelligence (AI);Ecosystems;Artificial neural network (ANN);Genetic algorithm (GA);Water resources management
    Date: 2015/11/30
    Issue Date: 2016-04-22 13:15:10 (UTC+8)
    Publisher: Elsevier BV
    Abstract: Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid methodology based on artificial intelligence (AI) techniques for quantifying riverine ecosystems requirements and delivering suitable flow regimes that sustain river and floodplain ecology through optimizing reservoir operation. This approach addresses issues to better fit riverine ecosystem requirements with existing human demands. We first explored and characterized the relationship between flow regimes and fish communities through a hybrid artificial neural network (ANN). Then the non-dominated sorting genetic algorithm II (NSGA-II) was established for river flow management over the Shihmen Reservoir in northern Taiwan. The ecosystem requirement took the form of maximizing fish diversity, which could be estimated by the hybrid ANN. The human requirement was to provide a higher satisfaction degree of water supply. The results demonstrated that the proposed methodology could offer a number of diversified alternative strategies for reservoir operation and improve reservoir operational strategies producing downstream flows that could meet both human and ecosystem needs. Applications that make this methodology attractive to water resources managers benefit from the wide spread of Pareto-front (optimal) solutions allowing decision makers to easily determine the best compromise through the trade-off between reservoir operational strategies for human and ecosystem needs.
    Relation: Journal of Hydrology 530, pp.634-644
    DOI: 10.1016/j.jhydrol.2015.10.024
    Appears in Collections:[水資源及環境工程學系暨研究所] 期刊論文

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