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Volume 11
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10.3390/f11010118
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Article

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

by
Viet-Hung Dang
1,
Nhat-Duc Hoang
2,
Le-Mai-Duyen Nguyen
3,
Dieu Tien Bui
4 and
Pijush Samui
5,6,*
1
Faculty of Information Technology, Duy Tan University, 03 Quang Trung, Da Nang 550000, Vietnam
2
Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809 - 03 Quang Trung, Danang 550000, Vietnam
3
Faculty of Electrical Engineering, Duy Tan University, 03 Quang Trung, Danang 550000, Vietnam
4
GIS Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, N-3800 Bø i Telemark, Norway
5
Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
6
Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
*
Author to whom correspondence should be addressed.
Forests 2020, 11(1), 118; https://doi.org/10.3390/f11010118
Submission received: 2 November 2019 / Revised: 11 January 2020 / Accepted: 14 January 2020 / Published: 19 January 2020
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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Abstract

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.
Keywords: random forest machine; landslide; geographic information system; machine learning; hybrid approach random forest machine; landslide; geographic information system; machine learning; hybrid approach

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MDPI and ACS Style

Dang, V.-H.; Hoang, N.-D.; Nguyen, L.-M.-D.; Bui, D.T.; Samui, P. A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility. Forests 2020, 11, 118. https://doi.org/10.3390/f11010118

AMA Style

Dang V-H, Hoang N-D, Nguyen L-M-D, Bui DT, Samui P. A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility. Forests. 2020; 11(1):118. https://doi.org/10.3390/f11010118

Chicago/Turabian Style

Dang, Viet-Hung, Nhat-Duc Hoang, Le-Mai-Duyen Nguyen, Dieu Tien Bui, and Pijush Samui. 2020. "A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility" Forests 11, no. 1: 118. https://doi.org/10.3390/f11010118

APA Style

Dang, V.-H., Hoang, N.-D., Nguyen, L.-M.-D., Bui, D. T., & Samui, P. (2020). A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility. Forests, 11(1), 118. https://doi.org/10.3390/f11010118

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