Landslide Susceptibility Analysis by Using Discriminant Analysis - A Case Study in KuoHsing,Central Taiwan

• Main Authors: Wei-Lien Chuang, 莊緯璉
• Other Authors: Chyi-Tyi Lee
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/33942275943949974293

碩士 === 國立中央大學 === 應用地質研究所 === 93 === This study follows the methodology and uses the original data of a landslide susceptibility project of Central Geological Survey, Taiwan (CGS). This study proceeds to check the data and to validate the model, and improves the treatment of some of the important factors. Reliability of weights among the factors was tested. The necessity of internal rating of each factor according to a terrain unit was also tested, and possible improvement was discussed. This study used different statistical software to validate the program we developed. I also compared the results evaluated by the logistic regression and the fuzzy neural network method so that the superiority among the three frequently used methods in landslide susceptibility analysis could be compared. Slope factor and terrain roughness factor were further studied. It includes : （1）Using high pass filter treatment to emphasize the local roughness of a terrain. （2）Using cumulative Weibull distribution to fit the curve of landslide ratio of slope factor. All factors were reproduced and redefine the internal rating of each factor were redefined. Samples for analysis were done by random sampling method from the non-landslide group so that they have approximately same number as the samples from landslide group. After the reprocessing and refinement of the factors, the result for each different event is significantly improved. Different random sampling results provide different weights. The result shows that the standard deviation of a weight for each factor is small and means the weights are stable and reliable. The results among the discriminant analysis, the logistic regression and the fuzzy neural network are comparable in overall accuracy. This indicates that the result from discriminant analysis is as good as the fuzzy neural network method which takes much time to train the sample. Internal rating of a factor according to the landslide ratio doesn’t affect the accuracy very much, but if the factor is rated according to landslide ratio can minimize the effect of data which are out of lower threshold or higher threshold, and emphasize the effect of the important range of the factor, and make a factor more effective in discriminant analysis. Whatever the internal rating of a factor is based on terrain units or not, the score of each factor will be normalized to a range between 0 and 1, and the result is not significantly different.