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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72504

    Title: 利用倒傳遞類神經網路預測污水下水道管網高水量異常模式-以某污水集污區豪大雨雨污混流為例
    Authors: 謝景年;Hsieh,Ching-Nien
    Contributors: 環境工程研究所在職專班
    Keywords: 污水管網;倒傳遞類神經網路;公開數據;Sewer network;BPN;Open data
    Date: 2016-07-18
    Issue Date: 2016-10-13 15:24:51 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 台灣早期住宅建設,並無強制要求將住宅內雨水與污水管線分流,導致汛期及豪大雨時,外水流入污水下水道系統,其管線設計已無法負荷現今極端氣候所帶來瞬間雨量,影響污水管網正常使用及後端污水處理廠操作處理,污水下水道系統形成疏洪渠道,污水處理廠淪為大型抽水站,不堪負荷甚至災損。
    ;Taiwan’s early residential construction didn’t enforce separation of sewage pipe and storm water pipe, which is why additional water along with underground water run into the same underground piping system during heavy rain and high tide situation. Such piping system can no long handle the amount of water from recent extreme weather condition, piping network becomes flood channel, and sewage plant station become a big pump station, resulting overloading or even catastrophic disaster.
    This study mainly explores the abnormal water level pattern in the sewage piping network. Data in the case study is recorded every hour, 24 sets a day, which equals to 8760 sets per year. It’s divided into three steps: First step is to install verification device, compare data, debug errors and sorting information. Second step is to apply the abnormal water level from the database to a supervised back propagation neural network model learning,applying abnormal rain fall water level mode, abnormal underground water mode, and all round estimation mode, to neural network model learning. Last step is to apply all finished mode into training data, entering network verification, and use Linear loop analysis to compare the real figure with the estimated figure.
    Results were promising to 80% accuracy. By establishing abnormal sewage collecting network system, allowing relevant institutes or officers to warn and adjust control figures and divert flood without paying expensive equipment to be installed at different sectors.
    Appears in Collections:[環境工程研究所碩士在職專班] 博碩士論文

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