Prediction of Landslide Hazards Using Spatial Data Mining – A Case Study for Gaoping River Basin

• Main Authors: Yi-Cen Zhuo, 卓怡岑
• Other Authors: Pai-Hui Hsu
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/92841185905318981166

碩士 === 國立臺灣大學 === 土木工程學研究所 === 101 === Due to the particular geographical location and geological condition, Taiwan suffers from many natural hazards, such as typhoons, flooding, landslides, land debris, and earthquakes, which often cause series property damages and even life losses. To reduce the damages and casualty, an effective real-time system for hazard prediction and mitigation is necessary. There are a number of factors leading to hazard, and the degrees of influence change with time and geospatial. Each factors interact each other too much to construct the model of hazards. Relevant departments have collected rich data about history hazards, including static space data and dynamic disaster data which have spatiality, time, multi-dimensional, large quantities, complexity, and spatial characteristics. It is one of research topics to find knowledge hiding in a large amount of spatial data. The spatial characteristic includes spatial auto-correlation and spatial heterogeneity. Because of spatial auto-correlation, the factor of each unit is dependent. when dealing with spatial data by ignoring spatial characteristic, the result will be illogical or biased estimated. Spatial data mining is mining interesting knowledge, such as critical rainfall of landslide, from a great quantity of spatial data. The study case is about the landslide at Gaoping river basin in Typhoon Haitang, and the grids of Gaoping river basin will be counted Global Moran''s I, Local Moran''s I, and G statistic to confirm spatial aggregation. Because all factor have spatial aggregation, critical rainfall needs to be found by models which combine logistic regression with spatial weight matrix. After testing with logistic regression, multilayer perception, and decision tree, spatial weight matrix and the buffer zone around a landslide site can raise prediction accuracy. When examining the accuracy of critical rainfall with Typhoon Morakot, it is over-predicted because not only Typhoon Morakot is extreme climate but also rainfall type is too complex to be described by one rainfall factor. Therefore, for estimating better critical rainfalls, training samples must include data of many typhoons and the model should consider the concept of spatial auto-correlation, and then we can give early-warning by the critical rainfalls and weather forecast.