台灣地區因破碎的地質構造，常因降雨、地震等觸發山崩造成災害。近年來因遙測技術以及地理資訊系統的蓬勃發展，山崩的分析已有不錯的監控與評估成果，並且累積了豐富的研究資料。為了直接利用、整合眾多不同來源的調查資料，找出隱含在資料中的知識，以利下一步運用。有許多針對具空間屬性資料的分析方法，能從大量的山崩調查資料中萃取出幫助辨識崩塌的有效法則。
本研究蒐集2004年至2008年颱風季節時的石門水庫集水區崩塌資料以及相關遙測及空間資料，主要有高解析度衛星影像、DTM資料、土地利用類別、地文描述資料(如河流、斷層)等。透過資料前處理與轉換，結合資料挖掘的學習法，找出山崩與環境因子間的關聯，建立降雨觸發的山崩特性推論模型，了解影響山崩的重要因子。而決策與規則訓練模型與測試成果，可調整資料以提升辨識精度。本研究所建立之決策樹針對2004年艾利颱風於研究區內之測試資料進行自動化崩塌地辨識，精度可提升至79%。本研究成果顯示，以資料挖掘技術對於遙測影像及GIS資料進行颱風豪雨誘發之崩塌地辨識為可行的方法。

The fractural geological conditions in Taiwan have caused serious landslides in mountainous regions after typhoon or earthquake every year. Remote sensing and other spatial data have been used successfully to evaluate and monitor landslide hazards. Satellite remote sensing and GIS-based data are effective sources to obtain information about environmental conditions covering large areas with high spatial details. For landslide related issues, the effect of environmental characteristics on the probability of landslide is an important factor and commonly used to predict landslide risks. In addition, other spatial data, such as digital terrainn model (DTM), land-cover types, vegetation, soil, and other natural and man-made factors may all contribute to the prediction of landslide susceptibility. This study utilizes data mining techniques to analyze complicated datasets in order to understand landslide risks in the Shihmen Reservoir watershed located in northern Taiwan.
An inventory of collected known landslides caused by typhoons from 2004 to 2007 in the study site is used as training data. Decision rules for detecting landslide from selected attributes have been established. The rules are applied to predict landslides induced by typhoons. The rules constructed from decision tree algorithms are refined to improve the classification accuracy. The identification accuracy is about 79% for the test data with 2004 Aere typhoon. With the developed algorithms and data mining techniques, landslides induced by heavy rainfall can be mapped efficiently from remotely sensed images and geo-spatial analysis.

田慶久、閩祥軍，1998，「植被指數研究進展」，地球科學進展，第13期，第4卷，第327-333頁。
朱聖心，2001，「應用地理資訊系統製作地震及降雨所引致之山崩危險圖」，碩士論文，國立臺灣大學土木工程學研究所，共169頁。
吳宗曄，2005，「空間資料探勘與知識產生-以建立崩坍敏感性評估模式為例」，碩士論文，國立台灣大學土木工程研究所，共139頁。
沈哲緯、林彥享、鄭錦桐、 邵國士、紀宗吉、張閔翔，2008，「運用資料採礦方法進行崩塌類型分類與山崩潛勢分析-以國姓、東勢、新社圖幅之斜坡單元為例」，2008年岩盤工程研討會，台灣台北，十月三十日至十月三十一日。
林彥享，2003，「運用類神經網路進行地震誘發山崩之潛感分析」，碩士論文，國立中央大學應用地質研究所，共89頁。
高申錡，1994，「阿里山公路沿線公路邊坡崩塌與雨量關係之研究」，碩士論文，國立成功大學資源管理研究所，共84頁。
張石角，1993，「山坡地調查規劃、評估其崩塌預測與治理」，行政院農委會專題研究。
莊緯璉，2005，「運用判別分析進行山崩潛感分析之研究-以臺灣中部國姓地區為例」，碩士論文，國立中央大學應用地質研究所，共178頁。
陳振華、潘國樑，1985，「台北市山坡地住宅區環境地質調查研究」，工研院能源與礦業研究所報告，第229號，共385頁。
陳俊賓，2005，「資料挖掘技術應用於外來入侵植物研究(以恆春地區銀合歡為例)」，碩士論文，國立中央大學土木工程研究所，共123頁。
陳良健、饒見有，2006，「95年度-以光學衛星影像輔助長期監測石門水庫集水區土地開發利用情形」，經濟部水利署北區資源局。
廖啟雯，2004，「機率式地震誘發山崩危害度分析–以國姓地區為例」，博士論文，國立中央大學地球物理研究所，共108頁。
榮峻德、孫志鴻、李萬凱，2005，「普查資料之空間資料挖掘研究–以台北市便利商店區位分析為例」，臺灣地理資訊學刊,第2期, 第45-46頁.
蔡富安、饒見有， 2008，「96-98 年光學衛星影像長期輔助監測石門水庫集水區土地開發利用情形」，經濟部水利署北區資源局。
鄭傑銘，2003，「應用GIS進行豪雨及地震引致山崩之潛感性分析」，碩士論文，國立台灣大學土木工程研究所，共210頁。
魏正岳、李錦發、李錫堤、黃健政，2005，「山崩調查與山崩潛感分析」，經濟部中央地質調查所，第62-72頁.
鐘意晴，2008，「區域性山崩潛感分析方法探討-以石門水庫集水區為例」，碩士論文，國立中央大學地球物理研究所，共172頁。
Ayalew, L., and H. Yamagishi, 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains, central Japan. Geomorphology, 65(1-2), pp.15-31.
Casadel, M., W.E. Dietrich, and N.L. Miller, 2003. Testing a model for predicting the timing and location of shallow landslide initiation in soil-mantled landscapes. Earth Surface Processes and Landforms, 28, pp.26-925.
Crosta, G., and P. Frattini, 2003. Distributed modelling of shallow landslides triggered by intense rainfall. Natural Hazards and Earth System Sciences, 3, pp.81-93.
Cruden, D.M., and D.J. Varnes, 1996. Landslide types and processes In: K.A. Turner and R.L. Schuster, Editors, Landslides: investigation and mitigation, Transport Research Board Special Report, 247, pp.36-75.
Dahal, R.K., S. Hasegawa, A. Nonomura, M. Yamanaka, S. Dhakal, and P. Paudyal, 2008. Predictive modelling of rainfall-induced landslide hazard in the lesser himalaya of nepal based on weights-of-evidence. Geomorphology, 102(3-4), pp.496-510.
Dietrich, W.E., R. Reiss, L. Hsu, and D.R. Montgomery, 1995. A process-based model for colluvial soil depth and shallow landsliding using digital elevation data, Hydrological Processes, 9, pp.383-400.
Dietrich, W.E., M. Casadei, and N. Miller, 2002. Linking hillslope hydrology to geomorphic processes: Soil thickness, shallow landsliding and the importance of high resolution topographic data obtained from airborne laser swath mapping. AGU Spring Meeting, 04.
Ester, M., Kriegel, H.P., and J. Sander, 2001. Algorithm and applications for spatial data mining, Geographic data mining and knowledge discovery (edited by H.J. Miller and J. Han), pp. 160-187. Taylor and Francis, London.
Flentje, P., D. Stirling, and R.N. Chowdhury, 2007. Landslide susceptibility and hazard derived from a landslide inventory using data mining -an Australian case study, In Proceedings of the First North American Landslide Conference, Landslides and Society: Integrated Science, Engineering, Management and Mitigation, Vail, Colorado. June 3-8.
Guzzetti, F., P. Reichenbach, M. Cardinali, M. Galli, and F. Ardizzone, 2005. Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1-4), pp.272-299.
Iverson, R.M. 2000. Landslide triggering by rain infiltration. Water Resources Research, 36(7), pp.1897- 1910.
Kanungo, D.P., M.K. Sarkar, S., and R.P. Gupta, 2006. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85(3-4), pp.347-366.
Kelarestaghi, A., 2003.Investigation of Effective Factors on Landslides Occurrence and Landslide Hazard Zonation–Case Study Shirin Rood Drainage Basin, Sari, Iran. Proceeding of the Map Asia 2003 Conference, pp.13-15.
Kheir, R.B., P.K.Bøcher, M.H.Greve, and M.B.Greve, 2010. The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data. Hydrology and Earth System Sciences Discussions, 7, pp.389-416.
Koperski, K., J. Han, and J. Adhikary. 1999. Mining knowledge in geographic data. In Communications. ACM (to appear).
Lee, C.T., C.C. Huang, J.F. Lee, K.L. Pan, M.L. Lin, and J.J. Dong, 2008. Statistical approach to earthquake-induced landslide susceptibility. Engineering Geology, 100(1-2), pp.43-58.
Li, D., K. Di, and D. Li, 2000. Land use classification of remote sensing image with GIS data based on spatial data mining techniques. International Archives of Photogrammetry and Remote Sensing, 33(B3), pp.238-245.
Meusburger, K., and C. Alewell, 2008. Impacts of anthropogenic and environmental factors on the occurrence of shallow landslides in an Alpine catchment (Urseren Valley, Switzerland). Natural Hazards and Earth System Sciences, 8, pp.509-520.
Miller, H.J., and J. Han, 2001. Geographic data mining and knowledge discovery, pp. 3-32. London: New York : Taylor & Francis.
Montgomery, D.R. and W.E. Dietrich, 1994. A physically based model for the topographic control of shallow landsliding. Water Resources Research, 30(4), pp.1153-1171.
Nadjim, C., K. Zeitouni, and A. Boulmakoul, 2002. A decision tree for multi-layered spatial data. In Proceeding of 2002 International Symposium on Geospatial Theory, Processing and Application Symposium, Ottawa, Canada. July 9-12, 10 pages.
Ohlmacher, G.C., and J.C. Davis, 2003 . Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology, 69, pp.331-343.
Qi, F., and A.X., Zhu, 2003. Knowledge discovery form soil maps using inductive learning , International Journal of Geographical Information Science, 17(8), pp.771-795.
Qi, J., A. Chehbouni, A.R. Huete, Y.H. Kerr, and S. Sorooshian, 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), pp.119-126.
Quinlan, J.R. 1986. Induction of decision trees, Machine Learning, 1, pp.81-106.
Quinlan, J.R., 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
Satio, H., D. Nakayama, and H. Matsuyama, 2009. Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan. Geomorphology,109 (3-4), pp.108-121.
Sidle, R.C., A.J. Pearce, and C.L. O''Loughlin, 1985. Hillslope stability and land use, Water Resources Monograph, 11, pp.140-141.
Varnes, D.J., 1984. Landslide hazard zonation: A review of principles and practice, UNESCO Press, Paris. pp.63-65.
Wan, S., T.C. Lei, and T.Y. Chou, 2009. A novel data mining technique of analysis and classification for landslide problems. Natural Hazards, 52(1), pp.211-230.
Ward, T.J., R. Li., and D.B. Simons, 1982. Mapping landslide hazard in forest watershed, Journal of Geotechnical Engineering Division, ASCE, 108(GT2), pp. 319-324.
Wieczorek, G.F., 1987. Effect of rainfall intensity and duration on debris flows in central Santa Cruz Mountains, California. In Debris Flows/ Avalanches: Process, Recognition, and Mitigation (edited by J. E. Costa and G. F. Wieczorek), Reviews in Engineering Geology, Geological Society of America, Boulder, Colo., pp. 93-104.
Wu, W., and R.C. Sidle, 1995. A distributed slope stability model for steep forested basins. Water Resource Research, 31, pp.2097-2110.
Yamagishi, H., L. Ayalew, and K. Kato, 2005. Characteristics of the recent landslides in the Mid Niigata region-comparison between the landslides by the heavy rainfall on 13 July 2004, and by the intensive earthquakes on 23 October 2004. Landslides, 3, pp.181-185.
Zhou, C.H., C.F. Lee, J. Li, and Z.W. Xu, 2002. On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong. Geomorphology, 43(3-4), pp.197-207.