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Article

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

by 1,2, 1,2, 1,2,* and 1,2
1
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2
Academy of Disaster Reduction and Emergency Management, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editor: Mariano Lisi
Remote Sens. 2021, 13(8), 1572; https://doi.org/10.3390/rs13081572
Received: 26 March 2021 / Revised: 12 April 2021 / Accepted: 14 April 2021 / Published: 18 April 2021
Southeast Asia (SEA) is a region affected by landslide and wildfire; however, few studies on susceptibility modeling for the two hazards together have been conducted for this region, and the intersection and the uncertainty of the two hazards are rarely assessed. Thus, the intersection of landslide and wildfire susceptibility and the spatial uncertainty of the susceptibility maps were studied in this paper. Reliable landslide and wildfire susceptibility maps are necessary for disaster management and land use planning. This work used three advanced ensemble machine learning algorithms: RF (Random Forest), GBDT (Gradient Boosting Decision Tree) and AdaBoost (Adaptive Boosting) to assess the landslide and wildfire susceptibility for SEA. A geo-database was established with 2759 landslide locations, 1633 wildfire locations and 18 predictor variables in total. The performances of the models were assessed using the overall classification accuracy (ACC), Precision, the area under the ROC (receiver operating curve) (AUC) and confusion matrix values. The results showed RF performs superior in both landslide (ACC = 0.81, Precision = 0.78 and AUC= 0.89) and wildfire (ACC= 0.83, Precision = 0.83 and AUC = 0.91) susceptibility modeling, followed by GBDT and AdaBoost. The overall superiority of RF over other models indicates that it is potentially an efficient model for landslide and wildfire susceptibility mapping. The landslide and wildfire susceptibility were obtained using the RF model. This paper also conducted an overlay analysis of the two hazards. The uncertainty of the susceptibility was further assessed using the coefficient of variation (CV). Additionally, the distance to roads is relatively important in both landslide and wildfire susceptibility, which is the most important in landslides and the second most important in wildfires. The result of this paper is useful for mastering the whole situation of hazard susceptibility and proves that RF is a robust model in the hazard susceptibility assessment in SEA. View Full-Text
Keywords: Southeast Asia; landslide; wildfire; susceptibility; ensemble machine learning Southeast Asia; landslide; wildfire; susceptibility; ensemble machine learning
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MDPI and ACS Style

He, Q.; Jiang, Z.; Wang, M.; Liu, K. Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods. Remote Sens. 2021, 13, 1572. https://doi.org/10.3390/rs13081572

AMA Style

He Q, Jiang Z, Wang M, Liu K. Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods. Remote Sensing. 2021; 13(8):1572. https://doi.org/10.3390/rs13081572

Chicago/Turabian Style

He, Qian, Ziyu Jiang, Ming Wang, and Kai Liu. 2021. "Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods" Remote Sensing 13, no. 8: 1572. https://doi.org/10.3390/rs13081572

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