The research integrates the field of data mining, machine learning, hydrology, optical remote sensing. Through the development of the spatial statistical regression model—Typhoon Type Index (TTI) and the automatic collapse identification system based on the Random Forest algorithm, the relation between the spatial distribution of typhoon features and collapses was built. Additionally, a hierarchical cluster analysis method based on spatial correlation has been developed to efficiently classify plenty of typhoon events according to the characteristics of wind and rain, so as to establish an early warning prediction model of slope disasters under typhoons.This interdisciplinary research integrated plenty of fields, including hydrology and water resources, environmental science, disaster prevention, application of big data analysis, climate change, Geoscience, environmental remote sensing, geographic information system, and risk assessment. Pioneering the technique to discuss the spatial distribution of slope disasters and agricultural damage, corresponding to different types of typhoons, and combined with satellite image analysis to establish a prediction model of the spatial and scale distribution of slope disasters under different typhoon characteristics.Through interdisciplinary integration, in addition to establishing the prediction model of typhoon features spatial distribution and slope disasters, and the original Typhoon Type index (TTI), it also extended the analysis of the risk of agricultural loss caused by different types of typhoons. The results and technologies mentioned above allow the government, research agencies, and related industries to refer and apply. Associated applications are slope disaster identification, disaster situation analysis, disaster prevention and response, and disaster risk early warning, etc.