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ISPRS Int. J. Geo-Inf. 2016, 5(10), 191; doi:10.3390/ijgi5100191

Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

1
School of Electronics and Information Engineering, Hebei University of Technology, No. 5340 Xiping RD, Beichen District, Tianjin 300401, China
2
Information Center, Tianjin Chengjian University, No. 26 Jinjing RD, Xiqing District, Tianjin 300384, China
3
School of Information Science and Engineering, University of Jinan, No. 336 West Road of Nan Xinzhuang, Jinan 250022, China
4
Centre of Intelligent and Networked System, Central Queensland University, Bruce Highway, North Rockhampton, Queensland 4701, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Jason C. Hung, Yu-Wei Chan, Neil Y. Yen, Qingguo Zhou and Wolfgang Kainz
Received: 1 June 2016 / Revised: 27 September 2016 / Accepted: 8 October 2016 / Published: 13 October 2016
(This article belongs to the Special Issue Advanced Geo-Information Technologies for Anticipatory Computing)
View Full-Text   |   Download PDF [4755 KB, uploaded 13 October 2016]   |  

Abstract

In this paper, we propose a multiple kernel relevance vector machine (RVM) method based on the adaptive cloud particle swarm optimization (PSO) algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the final kernel mapping. The weights and parameters of the multi-kernel function were optimized using the PSO algorithm. In addition, the convergence speed of the PSO algorithm was increased using cloud theory. To ensure the stability of the prediction model, the result of a five-fold cross-validation method was used as the fitness of the PSO algorithm. To verify the results, receiver operating characteristic curves (ROC) and landslide dot density (LDD) were used. The results show that the model that used a heterogeneous kernel (a combination of two different kernel functions) had a larger area under the ROC curve (0.7616) and a lower prediction error ratio (0.28%) than did the other types of kernel models employed in this study. In addition, both the sum of two high susceptibility zone LDDs (6.71/100 km2) and the sum of two low susceptibility zone LDDs (0.82/100 km2) demonstrated that the landslide susceptibility map based on the heterogeneous kernel model was closest to the historical landslide distribution. In conclusion, the results obtained in this study can provide very useful information for disaster prevention and land-use planning in the study area. View Full-Text
Keywords: particle swarm optimization; multiple kernel learning; relevance vector machine; landslide susceptibility; geographical information system (GIS) particle swarm optimization; multiple kernel learning; relevance vector machine; landslide susceptibility; geographical information system (GIS)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lin, Y.; Xia, K.; Jiang, X.; Bai, J.; Wu, P. Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China. ISPRS Int. J. Geo-Inf. 2016, 5, 191.

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