Abstract
Landslide susceptibility assessment is presently considered an effective tool for landslide warning and forecasting. Under the assessment procedure, a credible index weight can greatly increase the rationality of the assessment result. Using the Beijiang River Basin, China, as a case study, this paper proposes a new weight-determining method based on random forest (RF) and used the weighted linear combination (WLC) to evaluate the landslide susceptibility. The RF weight and eight indices were used to construct the assessment model. As a comparison, the entropy weight (EW) and weight determined by analytic hierarchy process (AHP) were also used, respectively, to demonstrate the rationality of the proposed weight-determining method. The results show that: (1) the average error rates of training and testing based on RF are 18.12% and 15.83%, respectively, suggesting that the RF model can be considered rational and credible; (2) RF ranks the indices elevation (EL), slope (SL), maximum one-day precipitation (M1DP) and distance to fault (DF) as the Top 4 most important of the eight indices, occupying 73.24% of the total, while the indices runoff coefficient (RC), normalized difference vegetation index (NDVI), shear resistance capacity (SRC) and available water capacity (AWC) are less consequential, with an index importance degree of only 26.76% of the total; and (3) the verification of landslide susceptibility indicates that the accuracy rate based on the RF weight reaches 75.41% but are only 59.02% and 72.13% for the other two weights (EW and AHP), respectively. This paper shows the potential to provide a new weight-determining method for landslide susceptibility assessment. Evaluation results are expected to provide a reference for landslide management, prevention and reduction in the studied basin.
Highlights
The occurrence frequency of natural disaster has increased in recent decades on the background of global warming [1,2,3,4,5,6,7,8,9]
Afterwards, the landslide susceptibility was assessed by combining the random forest (RF) weight and weighted linear combination method
The entropy weight (EW) and weight determined by analytic hierarchy process (AHP)
Summary
The occurrence frequency of natural disaster has increased in recent decades on the background of global warming [1,2,3,4,5,6,7,8,9]. Rainfall-induced landslides are considered one of the most common natural disasters resulting in significant economic damage and devastating loss of life [10,11]. Large-scale landslide occurrences are estimated to have led to at least 60,000 deaths with losses of more than US $9.7 billion worldwide from 1900 to 2016 [12]. Defining optimum preventive and palliative measures for appropriate landslide defense and management is essential within this context as landslide-induced losses may be reduced by nearly 90% at an estimated cost of 10.3% of the potential losses [17]. The exact location of such geological disaster implies all varieties of information of hazard inducing environment factors [19]
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