在現今氣候變遷的影響下，各種天然災害影響著人類生存的環境，具有付出代價最高、最致命天然災害的名單中，坡地崩塌災害榜上有名，其對人的社會帶來巨大的破壞以及環境威脅無法忽視。如何提供與崩塌地相關的重要空間資訊給行政單位制訂完備的減災法規以及政策甚為重要。本研究旨在使用機器學習方法於曾文溪流域的研究區域內，進行崩塌地潛勢預測，該流域為台灣最容易發生崩塌地的區域之一。因此，本研究以光達資料為時間基準，蒐集2009年至2015年曾文溪流域的相關空間數據，彙整出14個因子和崩塌地資料庫之資料，以隨機森林演算法建立崩塌地預測模型。同時採用像元式與物件式兩種分析方法建構模型，並進行比較。其結果顯示，基於像元式分析方法所獲得的高潛勢崩塌地的預測結果準確率為91.4％；以物件式分析方法所獲得的高潛勢崩塌預測結果準確率為86.5％。而Kappa係數評估結果，像元式分析方法達到0.83而物件式分析方法為0.73。此研究顯示，基於隨機森林演算法於曾文水庫集水區進行崩塌地潛勢預測時，透過於像元式分析中採用較粗分析尺度的移動窗口，以及於物件式分析中採用不同因子或變數進行物件分割獲取最佳分割單元，能對地形因子、水文因子、地質因子以及土地覆蓋因子進行潛勢預測分析中獲得較佳的成果。另外，兩種分析方法都顯示出，土地利用、地下水補給量和與舊崩塌地的距離，是研究區域中促發高潛勢崩塌地的最重要三個影響因素。

Landslides have been identified as one of the costliest and deadliest natural disasters, causing tremendous damage to humans and societies. Information regarding the spatial extent of landslides is thus important to allow officials to devise successful strategies to mitigate landslide hazards. This study aims to develop a machine-learning approach for predicting landslide areas in the Tsengwen River Watershed (TRW), which is one of the most landslide-prone areas in Southern Taiwan. According to the date of LiDAR data, various spatial datasets were collected from 2009 to 2015 to derive 14 factors and landslides ground references data used for landslide modeling with random forests (RF) algorithms. Two approaches, pixel-based and object-based, were adopted for building the landslide prediction model. The results of the high susceptibility landslide compared with ground reference data demonstrated the high accuracy of 91.4% for the pixel-based data set and 86.5% for the object-based data set. The kappa coefficient was evaluated as 0.83 for the pixel-based data set and 0.73 for the object-based data set, respectively. The findings achieved from this study validated our RF-based approach for landslide prediction in the Tsengwen Reservoir watershed of Taiwan, using optimal hydrological, geological, and topographical variables, coarser moving window sizes in the pixel-based approach, and selected variables segmentation in the object-based approach. Both approaches indicated that land-use/land-cover (LULC) types recharge of groundwater, and distance to old landslides were the most influential factors that trigger landslides in the study region.

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