| Summary: | 博士 === 國立臺灣大學 === 地理環境資源學研究所 === 93 === With the progress of information science and technology, the data collection march toward the automation and computerization. The fast accumulation of the spatial data, such as satellite image, GPS data recorder, mobile communication equipment and various kinds of location-based transaction, offer a large number of geo-referenced data. There include a lot of valuable information and knowledge among these data. How to refine out valuable knowledge from these data is a great subject faced at present. Data mining can help the policymaker to find out valuable knowledge from a large amount of data, but most application are still only limited to the analysis of the attribute data. It is difficult to deal with the spatial data. And though geographical information system is powerful in analyzing spatial data, but it lack the advanced ability to deal with sophisticated attribute data analysis. Because Taiwan is located in the earthquake zone, some areas go through a lot of earthquakes and periodic heavy earthquakes every year and cause serious landslide. If after going through the torrential rain again, will bring serious debris flow and cause great losses of the lives and properties. An earthquake of magnitude 7.3 on Richter scale occurred in the middle region of Taiwan on September 21, 1999. This earthquake caused more than two thousand people died, severe property loss, and a large number of landslides. A large number of landslide data and earthquake strong motion records were obtained for the experts and scholars to carry on the research of landslide influence of the earthquake. This research collects data of landslides triggered by Chi-Chi earthquake, and with the powerful data-processing function and spatial analysis ability of Geographic Information System (GIS), Data Mining modeling, the basic data of research region, and Chi-Chi earthquake strong motion records to establish the landslide database and data warehouse. A new strategy, which
combines several models based on different philosophy, not only reduce the uncertainty of predictive modeling, but also improve the accuracy. In our study, a Decision Tree, Artificial Neural Network, Bayes Classfier, and Exemplar-based Concept Learning were individually applied to a spatial data warehouse. The result of each model and two kinds of modeling-integration methods, including horizontal integration and vertical integration, were then evaluated. Furthermore, the spatial association patterns are typically not encoded in database, but are rather embedded within the spatial framework of the geo-referenced data. The analysis of the association pattern between the occurrence of Chi-Chi earthquake-induced landslide and background environmental characteristics is used as a case study to demonstrate the potential of spatial data mining techniques, like OLAP, association rule mining and Spearman rank correlation. With the analysis results, we derived a suspecious potential map and build the knowledge base and model base. Verification proofed the result to be good. So the analysis methods mentioned by this research are suitable for the risk assessment of landslide hazard triggered by earthquake and can be used as the tool for disaster mitigation decision support.
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