Journal
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Volume 26, Issue 4, Pages 747-770Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2011.613397
Keywords
expert system; landslide; data mining
Categories
Funding
- National Science Council [98-2625-M-275-001, 100-2410-H-275-009]
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Remote Sensing (RS) data can assist in the classification of landscapes to identify landslides. Recognizing the relationship between landform/landscape and landslide areas is, however, complex. Soil properties, geomorphological, and groundwater conditions govern the instability of slopes. Previous study of Wan (2009; A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Engineering Geology, 108, 237-251) used the maximum-likelihood classifier to classify the multi-category landslide image data. Unfortunately, the classification does not consider the geomorphologic condition. Accordingly, a Landslide Expert System was developed to modify these problems. The system uses multi-date SPOT image data to develop the landslide database. The threshold slope which becomes vulnerable to landslides is obtained by the K-means method. Then, an innovative Data Mining technique - Discrete Rough Sets (DRS) - is applied to obtain the core variables and their relevant thresholds. Finally, the Expert Knowledge Translation Platform (EKTP) is used to create the rules for classification. This study used a new approach called 'Rough Set Tree' to demonstrate the performance of the approach. The classification of landslide vulnerable areas, bare land, rock, streams, and water-body is greatly improved.
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