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姓名陳農彬(Nung-pin Chen) 查詢紙本館藏 畢業系所應用地質研究所 論文名稱應用類神經網路模式推估二維徑向收斂流場追蹤劑試驗縱向及側向延散度

(Estimation of longitudinal and transverse dispersivities in two-dimensional radially convergent flow field tracer test using artificial neural networks.)相關論文檔案[Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] [檢視] [下載]

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摘要(中)污染物於含水層中的傳輸行為常藉由移流-延散方程式描述，其中延散度為重要輸入參數，而推估延散度須先進行現地追蹤劑試驗，分析追蹤劑試驗濃度穿透曲線，並藉由數學模式所產生之標準曲線套配進行參數推估即可求得試驗場址之延散度，傳統標準曲線套配法需花費大量時間及具有曲線擬合不佳等缺點，造成實際應用的難題。本研究提出以類神經網路模式結合二維徑向收斂流場追蹤劑試驗模式建構倒傳遞套配模式(Back Propagation Neural Network Fitting Model, 簡稱BPNFM)提高延散度推估效率及精確度。結果顯示有效孔隙率介於 範圍內網路輸出值與目標輸出值之相對誤差均低於0.9 %；縱向延散度介於 範圍內相對誤差均低於3%；側向延散度介於 範圍內相對誤差均低於0.25 %，各套配模式在其適用範圍內均可獲得良好之輸出精確度。而套配效率上，倒傳遞套配模式可大幅縮短標準曲線套配法套配過程所耗費的時間，因此證實二維徑向收斂流場追蹤劑試驗套配模式可有效率地套配追蹤劑試驗數據，獲致可靠之延散度參數。

摘要(英)The transport process of solute in the aquifer as widely described by advection-dispersion equation (ADE). Dispersivities are the important input parameters for ADE. To obtain those parameters, a general methodology suggested to analyze the breakthrough curves (BTCs) plotted from tracer test. However, the artificial curve fitting causes lots of time consuming and errors. In this study, we try to promote one efficiency and accuracy method to estimate dispersivities. A back propagation neural network fitting model (BPNFM), combing with the neural network model and the two dimensional radially convergent flow tracer test is developed. Results show that the relative errors between target and network output data of effective porosity fall in the region from 0.05 to 0.5 is less than 0.9%; of longitudinal dispersivity distributed from 0.2m to 40m is less than 3%; of transverse dispersivity distributed from 0.033m to 6.427m is less than 0.25%. The consuming time can be reduced significantly by BPNFM, while the predicted parameters fall in the model setting, BPNFM is a accuracy and efficiency method for analyzing the dispersivities of the tracer tests.

關鍵字(中)★ 延散度

★ 類神經網路

★ 倒傳遞類神經網路套配模式

★ 追蹤劑試驗關鍵字(英)★ artificial neural network

★ dispresivities

★ tracer test

★ back propagation neural network fitting model論文目次中文摘要············································································i

英文摘要···········································································ii

誌謝················································································iii

目錄················································································iv

圖目錄·············································································vi

表目錄············································································viii

符號說明··········································································ix

一、前言···········································································1

1.1 研究動機·······································································1

1.2 文獻回顧·······································································3

1.2.1 追蹤劑試驗····························································3

1.2.2 徑向收斂流場追蹤劑試驗數學模式·····························6

1.2.3 類神經網路模式於地下水模擬之應用···························7

1.3 研究目的·····································································10

二、模式建立理論··························································11

2.1 移流-延散方程式····························································11

2.2 二維徑向收斂流場追蹤劑試驗模式···································18

2.3 類神經網路模式···························································26

三、結果與討論·······························································48

3.1 有效孔隙率套配模式BPN_?············································48

3.2 縱向延散度套配模式BPN_aL···········································53

3.3 側向延散度套配模式BPN_aT···········································58

3.4 現地追蹤劑試驗數據套配流程·········································63

3.5 假想現地追蹤劑試驗數據套配·········································65

3.6 綜合討論····································································72

四、結論與建議·······························································76

4.1 結論···········································································76

4.2 建議···········································································78

參考文獻········································································79

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指導教授陳瑞昇(Jui-sheng Chen) 審核日期2011-7-26 推文facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu 網路書籤Google bookmarks del.icio.us hemidemi myshare