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    Please use this identifier to cite or link to this item: http://chur.chu.edu.tw/handle/987654321/26649


    Title: 混合類神經與遺傳演算法於水庫優養最佳控制之研究
    Authors: 陳莉
    Chen, Li
    Contributors: 土木工程學系
    Civil Engineering
    Keywords: 優養化;類神經;遺傳演算法
    Eutrophication;Artificial Neural Network;Genetic Algorithms.
    Date: 2007
    Issue Date: 2014-06-26 20:44:02 (UTC+8)
    Abstract: 現今台灣水庫優養已成為嚴重的水質污染問題，水庫的大小、深度和形狀，以及日照的強
    度、氣溫和營養鹽等…都是影響水庫優養的原因。目前已有許多研究探討如何透過控制營養鹽
    濃度來減緩水體優養化，然大部分之研究乃基於點源或非點源污染之推估（如土壤萬用公式或
    AGNPS 模式）為線性及非時變，因此為考量污染源之時變性，本研究將嘗試針對不同組合之重
    要關係因子以類神經模式，建立水庫優養化預測模型。由於預測模型可能呈現非線性關係，因
    此本研究將結合遺傳演算法來探討降低磷自支流進入水庫中的可行性，使水庫不致造成優養的
    適當
    Recently, eutrophication has been a serious problem results in pollution of reservoirs’ quality. A
    lot of factors including the size, depth and shape of a reservoir as well as the intensity of sun light,
    could affect nutrients concentration in reservoirs.
    Appears in Collections:[Department of Civil Engineering] Seminar Papers

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