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The geological environment of Taiwan mainly contains steep topography and geologically fragile ground surface. Therefore, the vulnerable environmental conditions are prone to landslides during torrential rainfalls and typhoons. The rainfallinduced shallow landslide has become more common in Taiwan due to the extreme weathers in recent years. To evaluate the potential of landslide and its impacts, an evaluation method using the historical rainfall data (the hazard factor) and the temporal characteristics of landslide fragility curve (LFC, the vulnerability factor) was developed and described in this chapter. The LFC model was based on the geomorphological and vegetation factors using landslides at the Chen-Yu-Lan watershed in Taiwan, during events of Typhoon Sinlaku (September 2009) and Typhoon Morakot (August 2009). The critical hazard potential (Hc) and critical fragility potential (Fc) were introduced to express the probability of exceeding a damage state of landslides under certain conditions of rainfall intensity and accumulated rainfall. Case studies at Shenmu village in Taiwan were applied to illustrate the proposed method of landslide potential assessment and the landslide warning in practice. Finally, the proposed risk assessment for landslides can be implemented in the disaster response system and be extended to take debris flows into consideration altogether.

In an effort to manage the tedious slope maintenance works of mountain highways in Taiwan, a Slope Management System is developed based on a well designed MIS system in combination of the Geography Information System. The Slope Management System contains four major components: the inventory database, historical hazard information database, maintenance record database, and the expert system for mitigation strategy. All the information stored in the database and analyzed results can be visualized by taking the advantage of powerful display functions of GIS. In this paper, the detailed program structure of Slope Management System as well as operation example of a demo project will be introduced. Purpose of this paper is to introduce a helpful tool for slope mitigation and maintenance management.

In recent years, due to the frequent occurrence of extreme weather due to climate change, the Taiwan region has often suffered from landslides and debris flows in the past 20 years. This study used the ground surface vibration signals collected by the geophone from seven debris flow events in the Shenmu area. Data were processed to represent the time series of velocity and accumulated energy per second. Datasets were established for model training and validation. In this study, Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for comparison. After analyzing the data through balance processing (Synthetic Minority Oversampling Technique, SMOTE), a signal model of debris flow was established. The research results showed that the models using SVM and RF training had good accuracy, recall, and AUC values when choosing input data average of every 6 s and the 10-s time interval within which the data were marked as the occurrence of debris flow. The performance of SVM was better than that of RF after validation. Through the aforementioned research, the vibration signals of debris flow can be regarded as a reference factor, and the model established by the SVM method had acceptable performance and can be used for early-warning of debris flow.