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

Guangzhi Rong,Lina Han,Jiquan Zhang,Yichen Zhang,Si Alu,Kaiwei Li

doi:10.3390/w12092572

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.

Highlights

Among the most frequent natural disasters, landslides often result in several casualties and huge economic losses, seriously affecting social development and land use [1,2]
When the “Topples” and “Slides” nodes are both equal to 1, the probability of debris flows is 66.3%. These results reflect the probability of secondary hazards induced by primary hazards of topples-slides-debris flows disaster chain
This work construction of Landslide hazard mapping (LHM) is of great significance for the spatial differentiation analysis of landslide proposes the application of the GeoDetector and Bayesian network (BN) model for LHM in the case of Shuicheng County, hazards

Summary

Introduction

Among the most frequent natural disasters, landslides often result in several casualties and huge economic losses, seriously affecting social development and land use [1,2]. In 2016, 9710 landslides occurred in China, causing 370 deaths and approximately USD 457 million in direct economic losses [3]. Slides, and debris flows are the three most important types of landslides. Landslide hazard mapping (LHM) is an important tool for disaster prevention and mitigation because it can point out the vulnerable areas of landslides [4,5,6]. It is crucial to select suitable influencing factors and research methods to ensure the accuracy of the landslide hazard mapping and assessment. A disaster chain is a series of secondary disasters caused by primary disasters [7] and can be divided into concurrent disaster chains and serial disaster chains, according to the chain characteristics [8]

Objectives

Methods

Results

Discussion

Conclusion