Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data

Xiangxiang Zheng,Junchuan Yu,Yi Wang,Shanshan Wang,Ning Wang,Guizhou Wang,Guojin He,Zhaoying Yang

doi:10.3390/ijgi10040253

Abstract

The early identification of potential landslide hazards is of great practical significance for disaster early warning and prevention. The study used different machine learning methods to identify potential active landslides along a 15 km buffer zone on both sides of Jinsha River (Panzhihua-Huize section), China. The morphology and texture features of landslides were characterized with InSAR deformation monitoring data and high-resolution optical remote sensing data, combined with 17 landslide influencing factors. In the study area, 83 deformation accumulation areas of potential landslide hazards and 54 deformation accumulation areas of non-potential landslide hazards were identified through spatial overlay analysis with 64 potential active landslides, which have been confirmed by field verification. The Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were trained and tested through attribute selection and parameter optimization. Among the 17 landslide influencing factors, Drainage Density, NDVI, Slope and Weathering Degree play an indispensable role in the machine learning and recognition of landslide hazards in our study area, while other influencing factors play a certain role in different algorithms. A multi-index (Precision, Recall, F1) comparison shows that the SVM (0.867, 0.829, 0.816) has better recognition precision skill for small-scale unbalanced landslide deformation datasets, followed by RF (0.765, 0.756, 0.741), DT (0.755, 0.756, 0.748) and NB (0.659, 0.659, 0.659). Different from the previous study on landslide susceptibility and hazard mapping based on machine learning, this study focuses on how to find out the potential active landslide points more accurately, rather than evaluating the landslide susceptibility of specific areas to tell us which areas are more sensitive to landslides. This study verified the feasibility of early identification of landslide hazards by using different machine learning methods combined with deformation information and multi-source landslide influencing factors rather than by relying on human–computer interaction. This study shows that the efficiency of potential hazard identification can be increased while reducing the subjective bias caused by relying only on human experts.

Highlights

Introduction distributed under the terms andLandslides are a type of geological disaster that can cause serious casualties and huge property losses [1,2]
These studies often use Object-Oriented methods, Support Vector Machine (SVM), Random Forest (RF) and other machine learning methods to identify the occurrence of landslides and cataloging them; the regional landslide risk assessment is realized by different machine and ensemble learning models by modeling and analyzing the importance of influencing factors of landslide cataloging, and obtain landslide susceptibility maps [7,8,9,10,11,12]
Based on the multi period Sentinel-1 data and Gaofen-1 optical image in the data list of Table 1, the temporal InSAR processing method and human–computer interactive interpretation method based on expert experience were used respectively to obtain the spatial distribution of deformation accumulation area and suspected active landslide hazards in the study area

Summary

Introduction

Introduction distributed under the terms andLandslides are a type of geological disaster that can cause serious casualties and huge property losses [1,2]. Regional-scale studies mainly focus on three aspects: landslide detection, historical landslide cataloging, and regional landslide risk assessment These regional-scale studies rely on spectral features, spatial features, morphological attributes, and contextual information of landslides derived from multi-source remote sensing images. These studies often use Object-Oriented methods, SVM, RF and other machine learning methods to identify the occurrence of landslides and cataloging them; the regional landslide risk assessment is realized by different machine and ensemble learning models by modeling and analyzing the importance of influencing factors of landslide cataloging, and obtain landslide susceptibility maps [7,8,9,10,11,12]. Point-scale studies focus on retrospective analyses of the spatio-temporal evolution of a specific landslide disaster and provide data for monitoring an emergency and analyze a landslide disaster

Methods

Results

Conclusion