| Summary: | 碩士 === 長榮大學 === 土地管理與開發學系碩士班 === 103 === Two-thirds of the land in Taiwan is hillside land. In recent years, the human development is extending towards the hillsides due to the high population of plain area. With the adverse global climate, extreme rainfall often caused landslide and accompanied by the major damage of lives and properties. It is quite a burden to the government and the society. Therefore, the strikes and effects would surely be lessened if we can estimate the probability of rainfall-induced landslide occurrence and take action in disaster prevention.
We chose the Tsengwen river watershed in southern Taiwan as study region. We used Genetic Adaptive Neural Network (GANN) to classify satellite images before and after typhoon Soulik, Trami, and Kongrey that struck Taiwan and to get the information of the surface and disaster records. The geographic information system combined with digital elevation model and rainfall data was employed to establish database of potential factors of landslide which include the degree of land disturbance, nature environment and rainfall. This study developed an evaluation module for landslide potential according to optimum seeking theory through MATLAB platform. Then, the weights of potential factors of landslide were determined and the map of the potential of landslide in study area was plotted.
The results of the study show that the accuracy of the satellite images classification is at high level. For training and testing, the overall accuracy of evaluation module for landslide potential is up to 86%. In addition, the results predicted by the evaluation module match the actual situation of collapsed scene. Therefore, the proposed model can be applied in practice.
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