Abstract
Debris flow susceptibility mapping is considered to be useful for hazard prevention and mitigation. As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China. Therefore, this study attempted to evaluate and compare the performance of four state-of-the-art machine-learning methods, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Boosted Regression Trees (BRT), for debris flow susceptibility mapping in this region. Four models were constructed based on the debris flow inventory and a range of causal factors. A variety of datasets was obtained through the combined application of remote sensing (RS) and geographic information system (GIS). The mean altitude, altitude difference, aridity index, and groove gradient played the most important role in the assessment. The performance of these modes was evaluated using predictive accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). The results of this study showed that all four models were capable of producing accurate and robust debris flow susceptibility maps (ACC and AUC values were well above 0.75 and 0.80 separately). With an excellent spatial prediction capability and strong robustness, the BRT model (ACC = 0.781, AUC = 0.852) outperformed other models and was the ideal choice. Our results also exhibited the importance of selecting suitable mapping units and optimal predictors. Furthermore, the debris flow susceptibility maps of the Sichuan Province were produced, which can provide helpful data for assessing and mitigating debris flow hazards.
Highlights
Debris flow, a serious geological hazard, is defined as a mixture of water and a large number of loose materials like sediments, detritus, and muds, that cause great casualties and economic losses in mountainous areas all over the world [1,2,3]
The core of Logistic Regression (LR) modeling was the estimation of the regression coefficients using the maximum likelihood method
The results of the assessment showed that watersheds with high and very high debris flow susceptibility were distributed in the central mountainous region of the study area
Summary
A serious geological hazard, is defined as a mixture of water and a large number of loose materials like sediments, detritus, and muds, that cause great casualties and economic losses in mountainous areas all over the world [1,2,3]. Due to the complex natural conditions, South-West China is a typical area with active debris flow. About half of these debris flows took place in the high mountain zone of South-West China [4]. Geomorphological variations, heavy rainfalls, frequent seismic activities, and unreasonable land uses are responsible for triggering such a large number of debris flow in this region. Appropriate disaster mitigation and prevention solutions should be determined based on the debris flow susceptibility zoning
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