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

Yongliang Lin,Panpan Wu,Xiaoqing Jiang,Jianchuan Bai,Kewen Xia

doi:10.3390/ijgi5100191

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.

Highlights

Landslide susceptibility assessment always requires the consideration of many non-linear relation environmental factors, such as geomorphological, geological, hydrological and land cover data [1,2]
artificial neural networks (ANNs) and support vector machines (SVMs) have been proven to be more effective than the above methods for landslide susceptibility assessments
relevance vector machine (RVM) models using five kernel types based on the cloud PSO (CPSO) algorithm were applied to landslide susceptibility mapping of the low hill area of China’s Sichuan Province

Summary

Introduction

Landslide susceptibility assessment always requires the consideration of many non-linear relation environmental factors, such as geomorphological, geological, hydrological and land cover data [1,2]. SVM performs better than ANN at generalization, but its kernel needs to satisfy the Mercer conditions. It cannot directly estimate the prediction uncertainty. The relevance vector machine (RVM) proposed by Tipping [10] is a new Bayes probability model based on SVM, and its kernel does not need to satisfy the Mercer conditions [11]. The adaptive cloud PSO (CPSO) algorithm is one of the most efficient methods in improvement research It can both increase the convergence speed and ensure the diversity of a population [24,25,28]. RVM models using five kernel types based on the CPSO algorithm were applied to landslide susceptibility mapping of the low hill area of China’s Sichuan Province. The prediction performances of the five models were verified using a receiver operating characteristic (ROC) curve [32,33,34] and landslide dot density (LDD) [35,36]

Study Area

Multiple Kernel RVM

Influencing Factors of Landslides

Normalization Processing

Receiver Operating Characteristic Curve

Findings

Conclusions