結合TRIGRS與TOPMODEL預測淺層山崩; Coupling TRIGRS and TOPMODEL in Shallow Landslide Prediction

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Title:	結合TRIGRS與TOPMODEL預測淺層山崩;Coupling TRIGRS and TOPMODEL in Shallow Landslide Prediction
Authors:	李浩瑋;Hao-wei Li
Contributors:	應用地質研究所
Keywords:	一維入滲;淺層山崩;地形指數模式;Shallow Landslide;TOPMODEL;TRIGRS
Date:	2011-08-08
Issue Date:	2012-01-05 14:13:09 (UTC+8)
Abstract:	降雨入滲造成地下水位抬升，使土體的重量增加而抗剪強度降低，常成為淺層邊坡破壞之主要原因。以往常用之TRIGRS模式僅為一維入滲模式，未能考慮到地下水流的側向補注，TOPMODEL則能反應地形對集水區內各點水文反應之大小。本研究結合TRIGRS與TOPMODEL探討大漢溪流域之匹亞溪集水區於艾利颱風期間地下水位及安全係數之變化，評估可能發生滑動之地點。研究中將結合TRIGRS與TOPMODEL以及僅使用TRIGRS的模擬結果做比較，再將模擬結果以分類誤差矩陣及成功率曲線分析此兩個模式的適用性。結合TRIGRS與TOPMODEL的總體正確率為89.1%，成功率曲線下面積AUC為0.822，僅使用TRIGRS模式的總體正確率為87.4%，成功率曲線下面積AUC為0.787，顯示結合TRIGRS與TOPMODEL能更有效地解釋山崩分布。使用馬莎颱風雨量代入新模式預測馬莎風誘發山崩之結果顯示，預測率區曲線下面積AUC為0.813，成效尚佳，顯示此新模式具有降雨誘發淺層山崩之預測能力 Water infiltration can cause an increase in unit weight of soil and a decrease in strength of soil. In the past, the Transient Rainfall Infiltration and Grid-based Regional Slope-stability (TRIGRS) model uses an infinite-slope stability analysis with only one-dimensional vertical infiltration. The lateral flow, however, was not considered in the model, whereas the Topography-based hydrological MODEL (TOPMODEL) can describe the tendency of lateral water accumulation. This model has the merit of simplicity and the lateral flow processes using a limited amount of watershed topographic information. In the present study we use a conceptual framework of coupling TRIGRS and TOPMODEL to estimate groundwater table in a drainage basin, and conduct an infinite slope analysis to determine the instability of grid points. The study area was selected at Piya creek watershed of the Than River and the study aimed at predicting shallow landslides during the Typhoon Aere. The result indicates that the overall accuracy and the area under the success rate curve (AUC) of coupling TRIGRS and TOPMODEL are 89.1% and 0.822, respectively, whereas the overall accuracy and the AUC of TRIGRS only are 87.4% and 0.787, respectively. Therefore, considering lateral flow in the present proposed model does help increase accuracy for shallow landslide. The model is validated by the data set from the Matsa typhoon event. The result of validation shows that AUC of the prediction rate curves are 0.813, showing a good accuracy in predicting shallow landslides.
Appears in Collections:	[Graduate Institute of Applied Geology] Electronic Thesis & Dissertation

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