A Comparitive and Integrated Study of a Predictive Model in Spatial Data Mining--The Case of Chi-Chi Earthquake-induced Landslide Spatial Database
Resource
地理學報, 38, 093-109
Journal
地理學報
Journal Issue
38
Pages
093-109
Date Issued
2004-12
Date
2004-12
Author(s)
鄒明城
Abstract
Using a single model to forecast spatial phenomena will not produce good estimation in the prediction of individual pixels, even with good overall accuracy. A new strategy, which combines several models based on different philosophies, not only reduces the uncertainty of predictive modeling but also improves its accuracy. This study integrates a Decision Tree algorithm, the artificial Neural Network, the Bayes Classfier, and Exemplar-based concept Learning, with each individually applied to a spatial data warehouse. The results of each model and two kinds of modeling-integration methods, including horizontal integration and vertical integration, were then evaluated. In a case study, chose Chi-Chi earthquake-induced landslide to test the prediction accuracy and obtained good results. We believe that the same methodology can also been used in other cases of environment issues for which there is plentiful GIS digital data.
Subjects
預測型模式
資料探勘
地理資訊系統
地震山崩
Data mining
Geographic information system
Earthquake-induced landslide
Predictive model
Type
journal article
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