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Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining
Institute of Geophysics and Geomatics, China University of Geosciences / No. 388 Lumo Road, Wuhan, P.R. China
* Author to whom correspondence should be addressed.
Received: 17 December 2008; in revised form: 25 February 2009 / Accepted: 26 February 2009 / Published: 18 March 2009
Abstract: The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.
Keywords: Remote sensing image; landslide; forecast; Three Gorges
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Cite This Article
MDPI and ACS Style
Wang, X.; Niu, R. Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining. Sensors 2009, 9, 2035-2061.
Wang X, Niu R. Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining. Sensors. 2009; 9(3):2035-2061.
Wang, Xianmin; Niu, Ruiqing. 2009. "Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining." Sensors 9, no. 3: 2035-2061.