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Landslide susceptibility mapping based on particle swarm optimization of multiple kernel relevance vector machines: Case of a low hill area in Sichuan Province, China

journal contribution
posted on 07.03.2018, 00:00 by Y Lin, K Xia, X Jiang, Jianchuan BaiJianchuan Bai, P Wu
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.

Funding

Other

History

Volume

5

Issue

10

Start Page

1

End Page

16

Number of Pages

16

eISSN

2220-9964

Publisher

M D P I AG

Additional Rights

CC BY © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

Peer Reviewed

Yes

Open Access

Yes

External Author Affiliations

Hebei University of Technology; University of Jinan, China

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Journal

ISPRS International Journal of Geo-Information