Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)

Bahareh Kalantar,Biswajeet Pradhan,Shattri Mansor,Alireza Motevalli,Seyed Amir Naghibi

doi:10.1080/19475705.2017.1407368

Bahareh Kalantar, Biswajeet Pradhan + Show 3 more

Open Access

https://doi.org/10.1080/19475705.2017.1407368

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Abstract

ABSTRACTLandslide is a natural hazard that results in many economic damages and human losses every year. Numerous researchers have studied landslide susceptibility mapping (LSM), each attempting to improve the accuracy of the final outputs. However, few studies have been published on the training data selection effects on the LSM. Thus, this study assesses the training landslides random selection effects on support vector machine (SVM) accuracy, logistic regression (LR) and artificial neural networks (ANN) models for LSM in a catchment at the Dodangeh watershed, Mazandaran province, Iran. A 160 landslide locations inventory was collected by Geological Survey of Iran for this investigation. Different methods were implemented to define the landslide locations, such as inventory reports, satellite images and field survey. Moreover, 14 landslide conditioning factors were considered in the analysis of landslide susceptibility. These factors include curvature, plan curvature, profile curvature, altitude, slope angle, slope aspect, distance to faults, distance to stream, topographic wetness index, stream power index, terrain roughness index, sediment transport index, lithology and land use. The results show that the random landslide training data selection affected the parameter estimations of the SVM, LR and ANN algorithms. The results also show that the training samples selection had an effect on the accuracy of the susceptibility model because landslide conditioning factors vary according to the geographic locations in the study area. The LR model was found to be less sensitive than the SVM and ANN models to the training samples selection. Validation results showed that SVM and LR models outperformed the ANN model for all scenarios. The average overall accuracy of LR, SVM and ANN models are 81.42%, 79.82% and 70.2%, respectively.

Highlights

Landslide results in significant economic damage and human losses annually
The support vector machine (SVM) model fits the best hyperplane that can separate landslides from non-landslides effectively, some difficulty could be encountered in fitting the hyperplane when the predictors are not separable, indicating that the training sample selection can have effects on the SVM accuracy because landslide conditioning factors vary according to the geographic locations in the study area
The reason is that when selecting different landslide training data subsets, the number of selected landslides may not vary from one class to another

Summary

Introduction

Landslide results in significant economic damage and human losses annually. This phenomenon is described as a ground range wide movement, which includes rock falls, deep slope failures andB. Hjort and Marmion (2008) analysed the sample size effects on the accuracy of geomorphological model They analysed different sample size groups ranging from 20 to 600 samples based on generalized linear model, generalized additive model, generalized boosting method and artificial neural network (ANN) in two data settings, i.e. independent and split-sample approaches. They found that accuracy increases with the increase in sample numbers in all models, and the robust predictions level was reached with 200 observations. The sample size must not be too large, because this will likely violate the independent observations assumption because of spatial autocorrelation

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