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系統識別號 U0026-1408202010335600
論文名稱(中文) 類神經網路應用於降雨對邊坡地下水位升降之預測
論文名稱(英文) Application of Artificial Neural Networks to Predict Variation of Slope Water Table Induced by Rainfall
校院名稱 成功大學
系所名稱(中) 土木工程學系
系所名稱(英) Department of Civil Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 游子嫺
研究生(英文) Tzu-Hsien You
學號 N66074352
學位類別 碩士
語文別 中文
論文頁數 127頁
口試委員 指導教授-張文忠
口試委員-柯永彥
口試委員-倪勝火
口試委員-黃安斌
口試委員-陳家漢
中文關鍵字 類神經網路  依時地下水位預測  邊坡監測  資料時序分析 
英文關鍵字 Artificial neural network  Groundwater level forecasting  Slope monitoring  Time series analysis 
學科別分類
中文摘要 本研究以2018至2019年於臺20線52公里處之邊坡場址所架設的物聯網自動測站,蒐集監測之數據以進行邊坡因降雨引致地下水位升降分析。研究以為期兩年之監測成果訓練類神經網路,以監測到的雨量、水位資訊做為輸入特徵來預測未來特定時間段內之地下水位變化為目的。利用場址監測的雨量、水位、含水量計,並擷取特定時間點下的降雨序列、累積雨量(特定深度的含水量值)、水位高程變化和非飽和層厚度作為模型的特徵輸入,各因子分別對應至降雨總水量、非飽和層平均含水量、上游滲流補注變化與垂直入滲逕流長度。以適用於高度非線性關係且具有明顯對應值的倒傳遞神經網路架構進行水位預測模型的建立,並利用監測數據驗證,界定適用於水位預測的特徵因子範圍、模型架構的神經元個數及網路修正權重的演算方式。此類神經網路可供此場址以即時監測成果做水位的升降預測,後續可結合極限平衡分析以評估邊坡安全係數變化,提供相關負責單位進行預警。
英文摘要 Ground-water levels are a principal factor of slope instability and of significance in other geotechnical engineering problems. In this study, we propose a backpropagation neural network with 10-fold cross validation method for predicting the ground-water level induced by rainfall, the site was selected at South Cross-Island Highway 52K and use the value of monitoring system with ground-water level, soil water content and rainfall measurement. Furthermore, correlation analysis is used to find the potential input variables range for the predicting model, such as Pearson correlation coefficient and partial auto-correlation are applied. In this study, we choose four factor as the model input, such as hourly time series of rainfall and borehole water-level, depth of unsaturated zone and unsaturated zone moisture content via 4 different algorithms like the one step algorithm, the scaled conjugate gradient method, the Bayesian algorithm and the Levenberg-Marquardt method to find the best combination for predicting future 12 hours ground-water level. It was found that to obtain satisfactory results, the network using 4 hours of antecedent precipitation, 3 hours of antecedent borehole water-level, the time unsaturated zone thickness and turning 48 hours effective accumulated precipitation into unsaturated zone moisture content performed well in simulating the future 12 hours water-level.
論文目次 摘要 I
致謝 VIII
目錄 IX
表目錄 XI
圖目錄 XII
第一章、 緒論 1
1.1 研究動機與目的 2
1.2 研究方法與流程 2
第二章、 文獻回顧 4
2.1 降雨特徵 4
2.1.1 降雨事件之定義 4
2.1.2 前期降雨 5
2.1.3 降雨延遲 6
2.2 降雨對地下水位影響 9
2.2.1 影響地下水位的因子 9
2.2.2 降雨的入滲與補注 10
2.2.3 水位升降預測解析解 12
2.3 類神經網路 14
2.3.1 起源 14
2.3.2 類神經網路應用於水位預測 15
第三章、 研究方法 17
3.1 類神經網絡模型 17
3.1.1 類神經網路模型架構 17
3.1.2 類神經網路模式分類 20
3.2 倒傳遞神經網路 22
3.2.1 倒傳遞網路運算流程 23
3.2.2 倒傳遞網路演算法 27
3.2.3 特徵值的正規化處理 31
3.2.4 預測結果評估 33
3.2.5 數據劃分 34
3.3 資料相關性分析 37
3.3.1 時間序列分析 38
3.4 模型輸入因子 40
第四章、 研究結果與分析 45
4.1 研究區域與場址概述 45
4.1.1 氣候狀況 45
4.1.2 地質狀況 46
4.2 資料來源與前處理 46
4.3 場址特徵 51
4.3.1 水位變化趨勢 52
4.3.2 降雨延遲效應 57
4.4 模型設定 62
4.4.1 模型樣本選取說明 62
4.4.2 模型組合模式與流程說明 62
4.4.1 模型組合輸入說明 66
4.5 模型評估結果 68
第五章、 結論與建議 77
5.1 結論 77
5.2 建議 78
參考文獻 79
附錄一 樣本資料庫 84
附錄二 PiSjWkD模型組合測試結果 98
附錄三 PiCfWkD模型組合測試結果 116
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