廣域崩塌偵測品質精進初探－以經典群集分析為例

IMPROVING REGIONAL LANDSLIDE DETECTIONS WITH A CLASSICAL CLUSTERING ALGORITHMS-A PRELIMINARY EXPLORATION

10.6652/JoCICHE.202111_33(7).0007

賴哲儇(Jhe-Syuan Lai)；黃俊議(Jun-Yi Huang)

群集分析 ； 資料探勘 ； 數值高程模型 ； 崩塌目錄 ； cluster analysis ； data mining ； digital elevation model ； landslide inventory

中國土木水利工程學刊

33卷7期（2021 / 11 / 01）

535 - 544

繁體中文

臺灣頻繁地發生降雨型坡地崩塌，如何運用崩塌目錄（或稱資料庫）探討崩塌成因是重要課題。自然坡地崩塌特徵可分為崩塌源頭與Run-out，前者是崩塌發生的主因，Run-out為後續反應。若將Run-out 範圍一併進行崩塌分析，可能使成果產生偏差。本研究結合數值高程模型衍生的地形因子與基於分群的K-means++資料探勘演算法，並以兩組實驗評估演算法分離崩塌源頭和Run-out特徵的可行性與實務應用性。成果顯示，K-means++分群之整體精度超過70%，甚至部分多邊形樣本成果高於90%，證實群集分析分離崩塌源頭與Run-out特徵的可行性。此外，成果亦顯示不同面積多邊形樣本之分群成效，突顯崩塌偵測升級至目錄製作的實務應用價值。

Landslides are frequently induced by heavy rainfall events in Taiwan. Landslide inventory (or database) is an important material to explore the occurrences of landslides. Natural terrain landslides usually include landslide source and run-out features. The landslide source is the major area of the occurrence, while the run-out feature reveals the subsequent reaction. The run-out area should be separated or eliminated from the landslide source areas in order to avoid the bias when modeling landslide behaviors. This study combined the topographic factors derived from the digital elevation model (DEM) with a cluster-based data mining algorithm to separate landslide source and run-out areas. Two experiments were designed for examining the feasibility and applicability of the k-means++ algorithm. Preliminary results revealed that the developed model can approximately reach 70% at least for overall accuracies, and some polygon cases were further higher than 90%. It demonstrated the feasibility of the cluster analysis to separate the landslide source and run-out features. In addition, the applicability of improving the landslide detections for producing the inventory can be observed by the different group samples based on different area sizes extracted from the landslide polygons.

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