地質、地形、氣候等影響容易導致土石流、沖蝕、崩塌等坡 地災害，並因為台灣地形的因素，水資源不易保存，水庫是我們重 要的飲水來源，不過過多的崩塌地，會導致水質濁度增加與水庫淤 積以致水庫的使用年限大幅的降低，故如何快速的監測水庫周圍崩 塌地乃一門重要的研究課題。本研究的主旨在以螞蟻聚類演算法進 行崩塌地的影像分析，其目的在改善聚類優化的問題。此方法的優 點在運算的過程簡單，且為非監督式方法，不需資料類別。其步驟 詳細的說可分為(a)初始螞蟻個數 (b)計算螞蟻選擇群聚 (c)更新費洛 蒙 (d)計算新群心(e)進行迭代。以此方法得到的結果為正確率為 73% ，並以傳統的支援向量機進行比較， 若以計算成本來說確有不 錯效果，故此法有助於崩塌地的影像分類問題的解決。

Geology, topography and climate can influence the occurrence of debris-flow, erosion and landslide. On the other hand, the terrain is complex that the water resource can be reserved very difficultly. The massive landslide will produce the turbid of the water quality increase and the deposit will become worse. Hence, the lifetime usage of the reservoir will be reduced. To develop a monitoring system to investigate the landslide surrounding reservoir is a crucial work. In this study, the ant-clustering algorithm is developed. The advantage of this algorithm is to effective cluster data into groups. The steps are (a)initialize the number of ants (b)compute the ants to each cluster center (c)renew the pheromone (d)recalculate the cluster center (e) do iterations. The accuracy rate of this method is 82% of landslide classification. The landslide thematic map is drawn and the position of occurrence place is shown. The advantage of this process is low cost and it is very effective comparing to other supervised learning approaches.