Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

Nhu, Viet-Ha; Mohammadi, Ayub; Shahabi, Himan; Ahmad, Baharin Bin; Al-Ansari, Nadhir; Shirzadi, Ataollah; Clague, John J.; Jaafari, Abolfazl; Chen, Wei; Nguyen, Hoang

doi:10.3390/ijerph17144933

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Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

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Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam

Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam

Department of Remote Sensing and GIS, University of Tabriz, Tabriz 51666-16471, Iran

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Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran

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Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 66177-15175, Iran

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Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia

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Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden

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Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran

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Department of Earth Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada

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Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran P.O. Box 64414-356, Iran

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College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, China

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Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an 710021, Shaanxi, China

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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

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Int. J. Environ. Res. Public Health 2020, 17(14), 4933; https://doi.org/10.3390/ijerph17144933

Received: 11 May 2020 / Revised: 16 June 2020 / Accepted: 1 July 2020 / Published: 8 July 2020

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

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Abstract

We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards. View Full-Text

Keywords: machine learning; AdaBoost; alternating decision tree; ensemble model; Cameron Highlands; Malaysia

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Nhu, V.-H.; Mohammadi, A.; Shahabi, H.; Ahmad, B.B.; Al-Ansari, N.; Shirzadi, A.; Clague, J.J.; Jaafari, A.; Chen, W.; Nguyen, H. Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. Int. J. Environ. Res. Public Health 2020, 17, 4933. https://doi.org/10.3390/ijerph17144933

AMA Style

Nhu V-H, Mohammadi A, Shahabi H, Ahmad BB, Al-Ansari N, Shirzadi A, Clague JJ, Jaafari A, Chen W, Nguyen H. Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. International Journal of Environmental Research and Public Health. 2020; 17(14):4933. https://doi.org/10.3390/ijerph17144933

Chicago/Turabian Style

Nhu, Viet-Ha, Ayub Mohammadi, Himan Shahabi, Baharin B. Ahmad, Nadhir Al-Ansari, Ataollah Shirzadi, John J. Clague, Abolfazl Jaafari, Wei Chen, and Hoang Nguyen. 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment" International Journal of Environmental Research and Public Health 17, no. 14: 4933. https://doi.org/10.3390/ijerph17144933

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