Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Abstract

Landslides are among the most frequent natural hazards in the world. Rainfall is an important triggering factor for landslides and is responsible for topples, slides, and debris flows—three of the most important types of landslides. However, several previous relevant research studies covered general landslides and neglected the rainfall–topples–slides–debris flows disaster chain. Since landslide hazard mapping (LHM) is a critical tool for disaster prevention and mitigation, this study aimed to build a GeoDetector and Bayesian network (BN) model framework for LHM in Shuicheng County, China, to address these geohazards. The GeoDetector model will be used to screen factors, eliminate redundant information, and discuss the interaction between elements, while the BN model will be used for constructing a causality disaster chain network to determine the probability and risk level of the three types of landslides. The practicability of the BN model was confirmed by error rate and scoring rules validation. The prediction accuracy results were tested using overall accuracy, Matthews correlation coefficient, relative operating characteristics curve, and seed cell area index. The proposed framework is demonstrated to be sufficiently accurate to construct the complex LHM. In summary, the combination of the GeoDetector and BN model is very promising for spatial prediction of landslides.

Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece

“…By incorporating this property of GeoDetector in hybrid GeoDIV model, among the fourteen conditioning factors initially collected, four of them (slope aspect, profile curvature, SPI, and mean annual rainfall) were identified as statistically insignificant and were not finally included in LS assessment. Similar factors were also eliminated as redundant in [14,23,24].…”

“…Although GeoDetector has been tested in various studies of health, social and environmental sciences [20][21][22] over the last few years, its use in landslide-related research has been quite limited. Since it provides an effective way to identify and eliminate redundant variables, GeoDetector has been used in a few relevant studies [14,23,24] for factor selection purposes.…”

Rapid Terrain Assessment for Earthquake-Triggered Landslide Susceptibility With High-Resolution DEM and Critical Acceleration

“…MCC was used to evaluate the cells in the ETL prediction maps relative to the reference landslide map. Several studies (Rong et al, 2020;Wang et al, 2020;Meena et al, 2021) have adopted MCC to evaluate landslide susceptibility maps. Rong et al (2020) obtained MCC 0.44 for a hazard map of rainfall-induced landslides in Shuicheng, China.…”

“…Several studies (Rong et al, 2020;Wang et al, 2020;Meena et al, 2021) have adopted MCC to evaluate landslide susceptibility maps. Rong et al (2020) obtained MCC 0.44 for a hazard map of rainfall-induced landslides in Shuicheng, China. Wang and Lin, 2010 compared four different landslide susceptibility mapping methods and found that MCC varied from 0.4 to 0.5.…”

Editorial for the Special Issue “Water-Induced Landslides: Prediction and Control”

“…In this context, Kok et al [6] proposed a methodology to establish the most vulnerable regions in the Cameron Highlands, and Long and De Smedt [7] performed an analysis of the rainfall-induced landslide susceptibility in a district of Vietnam. Finally, Rong et al [8,9] used a Bayesian model for mapping the rainfall-induced landslides in China. These studies showed that the landslide mapping may be a useful tool for disaster prevention and hazard mitigation.…”