Entropy-Based Hybrid Integration of Random Forest and Support Vector Machine for Landslide Susceptibility Analysis

Landslide susceptibility mapping is a crucial step in comprehensive landslide risk management. The purpose of the present study is to analyze the landslide susceptibility of Mandi district, Himachal Pradesh, India, based on optimum feature selection and hybrid integration of the Shannon entropy (SE)...

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Published inGeomatics (Basel) Vol. 1; no. 4; pp. 399 - 416
Main Authors Sharma, Amol, Prakash, Chander, Manivasagam, V.
Format Journal Article
LanguageEnglish
Published Calgary MDPI AG 01.12.2021
Subjects
Drainage
Earthquakes
Feature selection
Geographic information systems
Geology
Highway construction
Hydrology
landslide susceptibility
Landslides & mudslides
Machine learning
Mapping
performance matrices
random forest
Remote sensing
Rivers
Roads & highways
Shannon entropy
Soil erosion
support vector machine
Support vector machines
Wind
India
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Summary:Landslide susceptibility mapping is a crucial step in comprehensive landslide risk management. The purpose of the present study is to analyze the landslide susceptibility of Mandi district, Himachal Pradesh, India, based on optimum feature selection and hybrid integration of the Shannon entropy (SE) model with random forest (RF) and support vector machine (SVM) models. An inventory of 1723 rainfall-induced landslides was generated and randomly selected for training (1199; 70%) and validation (524; 30%) purposes. A set of 14 relevant factors was selected and checked for multicollinearity. These factors were first ranked using Information Gain and Chi-square feature ranking algorithms. Furthermore, Wilcoxon Signed Rank Test and One-Sample T-Test were applied to check their statistical significance. An optimum subset of 11 landslide causative factors was then used for generating landslide susceptibility maps (LSM) using hybrid SE-RF and SE-SVM models. These LSM’s were validated and compared using receiver operating characteristic (ROC) curves and performance matrices. The SE-RF performed better with training and validation accuracies of 96.93% and 88.94%, respectively, compared with the SE-SVM model with training and validation accuracies of 94.05% and 82.4%, respectively. The prediction matrices also confirmed that the SE-RF model is better and is recommended for the landslide susceptibility analysis of similar mountainous regions worldwide.
ISSN:2673-7418
2673-7418
DOI:10.3390/geomatics1040023