A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

doi:10.3390/ijerph13050487

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A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

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Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

Author to whom correspondence should be addressed.

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These authors contributed equally to this work.

Academic Editor: Yu-pin Lin

Int. J. Environ. Res. Public Health 2016, 13(5), 487; https://doi.org/10.3390/ijerph13050487

Received: 23 February 2016 / Revised: 26 April 2016 / Accepted: 4 May 2016 / Published: 11 May 2016

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Abstract

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides. View Full-Text

Keywords: landslide susceptibility mapping; geographically weighted regression; support vector machine; particle swarm optimization; Three Gorges Reservoir

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Yu, X.; Wang, Y.; Niu, R.; Hu, Y. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China. Int. J. Environ. Res. Public Health 2016, 13, 487. https://doi.org/10.3390/ijerph13050487

AMA Style

Yu X, Wang Y, Niu R, Hu Y. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China. International Journal of Environmental Research and Public Health. 2016; 13(5):487. https://doi.org/10.3390/ijerph13050487

Chicago/Turabian Style

Yu, Xianyu, Yi Wang, Ruiqing Niu, and Youjian Hu. 2016. "A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China" International Journal of Environmental Research and Public Health 13, no. 5: 487. https://doi.org/10.3390/ijerph13050487

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A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

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