GEOSPATIAL DATA MINING AND KNOWLEDGE GENERATING - A Case Study of Constructing A Model of Assessing Landslide Susceptibility
Date Issued
2005
Date
2005
Author(s)
Wu, Tsung-Yeh
DOI
zh-TW
Abstract
In Taiwan, because of the influence of each environmental factors, landslides occur frequently around the basins. To ensure the safety for people residents, there are more and more researches to investigate, estimate, and predict landslides via the application of GIS. However, along with the process of accumulation of data and knowledge, it is urgent to employ existing knowledge to generate new knowledge to provide with further understanding of the machanisms and spatiotempotral distribution of landslides. For this reason, this research utilizes the technology of Geospatial Data Mining(GDM) to obtain relationships between the landslides and the environmental factors to deeply understand the landslides.
Firstly, the methodology is establshed by carrying out review of research of landslides and Geospatial Data Mining. Afterwards, the factors of model is selected, the method of spatial statistics and decision tree is built, the relationship between the environmental factors and landslides is summed up, and the environmental characters of landslides are described with the form of regulations.. This research develops a program of Arc Object to classify landslides data. Finally, the simulation results are evaluated and the conclusions and suggestions are proposed.
The results show that the highest accuracy rate of the predicting model reaches to 88.46% while the lowest one is 73.08% by using the four classifications generated by decision tree to classify the data to predict the distribution of landslides in the area under investigation.
In the future, since the validity and precision of data of assessing landslide susceptibility is very important, the result of simulation can be improved by updating the data or combining several classifications in the model.
Subjects
空間資料探勘
決策樹
崩坍敏感性
空間知識
Geospatial Data Mining(GDM)
Decision Tree
Landslide Susceptibility
Spatial Knowledge
Geographic Information System(GIS)
Type
thesis
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