A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in...

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Published inForests Vol. 11; no. 1; p. 118
Main Authors Dang, Viet-Hung, Hoang, Nhat-Duc, Nguyen, Le-Mai-Duyen, Bui, Dieu Tien, Samui, Pijush
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2020
Subjects
Algorithms
Artificial intelligence
Decision trees
Discriminant analysis
Earthquakes
geographic information system
Geological hazards
Geomorphology
hybrid approach
Hybridization
landslide
Landslides
Landslides & mudslides
Learning algorithms
Machine learning
Mapping
Popularity
Predictions
Rain
random forest machine
Support vector machines
Vietnam
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Summary:This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.
ISSN:1999-4907
1999-4907
DOI:10.3390/f11010118