建議

1. 本研究假設地質參數之空間變異性具有指數型態之共變異函數，然而依 據現地狀況之不同此假設並非永遠合適，後續可進一步探討當共變異函 數為其他型態時(如球型等)，對於區域可靠度所造成之影響。

2. 本研究目前僅考慮地質參數為風險因子(不確定性參數)，然對於降雨引 發淺崩塌之模擬仍有許多其他重要之條件或參數存在相當高之不確定 性，例如初始地下水位與土壤厚度等，建議後續可將風險分析方法擴充 考慮這些參數之不確定性。

3. 未來可以不同之不確定性分析方法計算安全係數之統計特性，例如改良 一階二次矩法(Advanced first-order second-moment method)、羅森布魯斯 點估計法(Rosenblueth’s point estimation)或哈爾點估計法(Harr’s point estimation)等，並比較不同方法應用於降雨引發坡地淺崩塌之適用性。

4. 不同格網點間安全係數之相關性主要包含兩項來源，即(1)地質參數在空 間上之相關性；以及(2)格網點發生崩塌後，其造成之土砂流動或地形變 化對鄰近格網點安定性之影響。本研究已針對前述第(1)項進行分析並用 以評估區域可靠度，後續可進一步分析第(2)項因素。

5. 本研究所提出之分析方法可計算在不同整治率下區域可靠度不再發生 崩塌之可靠度，建議後續可進一步考慮不同整治率所需之經濟成本，並 評估區域可靠度與工程成本間之競合關係(trade-off curve)，以作為決策 者擬定策略之參考。

6. 建議往後可進階考量水文條件之不確定性及不同降雨量下之重現期 距，配合本研究所提出之分析方法評估區域之年崩塌機率(the annual probability of a landslide occurring )。

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附錄Ａ 拉丁超立方取樣方法 (Latin Hypercubic Sampling ,LHS )

作業程序:

1. 在求解空間中，選取所需取樣之參數X，及取樣組數K，並決定輸入參 數之機率分佈型態。

2. 將參數X 可能的範圍劃分為K個區間，每個區間被取中的機率皆為 ¹ K。 3. 在劃分的K個區間內隨機取樣一個樣本值，可得到K個隨機樣本。

4. 任意排列此K個隨機樣本，即完成取樣。

附錄 B 正交轉換(Orthogonal Transformation)

由於相關係數矩陣為對稱且正定(positive definite)，利用 cholesky decomposition method 及特徵拆解法(eigenvector decomposition)將R_x進行拆

解:

其中，

Λ

為特徵值( eigenvalue )組成之對角矩陣；

V

為特徵向量(eigenvector) 組成之正規化矩陣。

1

X X 2

X =μ +σ ⋅ ⋅ Λ ⋅V Y (B.13)

式(B.13)為具相關性統計變數與無相關性統計變數之轉換式子。

圖B-1 正交轉換圖

在文檔中 降雨引發坡地淺崩塌之區域性風險分析研究 (頁 63-74)