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

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

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 b...

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Bibliographic Details
Main Authors: Yongliang Lin, Kewen Xia, Xiaoqing Jiang, Jianchuan Bai, Panpan Wu
Format: Article
Language:English
Published: MDPI AG 2016-10-01
Series:ISPRS International Journal of Geo-Information
Subjects:
particle swarm optimization
multiple kernel learning
relevance vector machine
landslide susceptibility
geographical information system (GIS)
Online Access:http://www.mdpi.com/2220-9964/5/10/191

Internet

http://www.mdpi.com/2220-9964/5/10/191

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