Debris Flow Susceptibility Assessment Using the Integrated Random Forest Based Steady-State Infinite Slope Method: A Case Study in Changbai Mountain, China

Abstract

Debris flow events often pose significant damage and are a threat to infrastructure and even livelihoods. Recent studies have mainly focused on determining the susceptibility of debris flow using deterministic or heuristic/probabilistic models. However, each type of model has its own significant advantages with some irreparable disadvantages. The random forest model, which is sensitive to the region where the terrain conditions are suitable for the occurrence of debris flow, was applied along with the steady-state infinite slope method, which is capable of describing the initiation mechanism of debris flow. In this manner, a random-forest-based steady-state infinite slope method was used to conduct susceptibility assessment of debris-flow at Changbai mountain area. Results showed that the assessment accuracy of the proposed random-forest-based steady-state infinite slope method reached 90.88%; however, the accuracy of just the random forest model or steady-state infinite slope method was only 88.48% or 60.45%, respectively. Compared with the single-model assessment results, the assessment accuracy of the proposed method improved by 2.4% and 30.43%, respectively. Meanwhile, the debris-flow-prone area of the proposed method was reduced. The random-forest-based steady-state infinite slope method inherited the excellent diagnostic performance of the random-forest models in the region where the debris flow disaster already occurred; meanwhile, this method further refined the debris-flow-prone area from the suitable terrain area based on physico-mechanical properties; thus, the performance of this method was better than those of the other two models.

Deep Learning-based Study on Assessment and Enhancement Strategy for Geological Disaster Emergency Evacuation Capacity in Changbai Mountain North Scenic Area

“…Changbai Mountain, being the highest volcano in China, possesses a steep topography rendering it susceptible to landslides, particularly during periods of intense rainfall, thereby posing a threat to the surrounding area [9][10][11][12][13] . Situated in the southeastern mountainous region of Jilin Province, China, the Northern Changbai Mountain Scenic Area spans geographic coordinates of 127°28' to 128°16' east longitude and 41°42' to 42°25' north latitude.…”

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

“…Among the 49 papers utilizing the surface evaluation units, all of the 12 categories were used, with the top three being topography factors (45), meteorology factors (40), and morphology factors (35). By contrast, in the 36 studies utilizing point evaluation units, morphology factors, fire factors, material source factors, and characteristics factors of past debris flow were not used; the top three with the highest number of papers reported were topography factor (34), hydrology factor (27), and human activity factor (24). This discrepancy may be due to morphology factors, material source factors, and characteristic factors of past debris flow being associated with surface evaluation units, and researchers were more inclined to utilize the surface evaluation units to predict the occurrence of postfire debris flow (fire factor utilized) based on ML.…”

“…In this study, the prediction of debris flow occurrence refers to whether debris flow occurs or the possibility of debris flow occurrence within a certain area based on the prediction model. The possibility of debris flow occurrence is generally characterized by the susceptibility [27,28] or hazard [29,30] level of the debris flow. Susceptibility refers to the possibility of debris flow occurrence in a certain evaluation unit, considering non-triggering factors such as topography, geomorphology, and surface cover characteristics.…”

Landslide Modeling in a Tropical Mountain Basin Using Machine Learning Algorithms and Shapley Additive Explanations

“…This category includes geotechnical methods such as the Newmark’s Method (Infinite Slope), Bishop's Method, Morgenstern Price Method, among others. Various studies have used deterministic models, as the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model (Ma et al, 2021; Marin et al, 2021), Shallow Slope Stability (SHALSTAB) model (Pradhan & Kim, 2015), Stability Index MAPping (SINMAP) model (Michel et al, 2014), and Steady-State Infinite Slope Method (SSIS) (Si et al, 2020).…”