Abstract
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.
Highlights
Southeast Asia, one of the most natural disaster-prone regions in the world, numerous natural hazards such as floods, earthquakes and heatwaves happen here every year [1,2,3,4].The landscape in mainland SEA is characterized by mountainous areas [5]
The results demonstrated that Random Forest (RF) exhibited the best performance among the three ensemble machine learning methods for the two hazards, determining that the RF model is more predictive than Gradient Boosting Decision Tree (GBDT) and AdaBoost in landslide and wildfire susceptibility modeling and mapping
The results showed that the RF model outperformed the other two models in both landslide and wildfire susceptibility modeling, and the exceptional potential of the RF model has been supported by other related research [8,49,90]
Summary
Southeast Asia, one of the most natural disaster-prone regions in the world, numerous natural hazards such as floods, earthquakes and heatwaves happen here every year [1,2,3,4]. The landscape in mainland SEA is characterized by mountainous areas [5]. Landslide is a main geological hazard in SEA, having damaging impacts on the safety of life and property [6,7,8]. Wildfires are frequent in SEA, in Indonesia, and the forests of SEA are represented as increasingly at-risk to fire [9,10]. Wildfires can cause various impacts, including a loss of biodiversity, loss of assets and damage to natural resources and agriculture areas [14,15]
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