Regional landslide hazard assessment through integrating susceptibility index and rainfall process

Abstract

Due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geological, fault, and lithology), it is difficult to assess the hazard of landslides at the regional scale quantitatively only considering rainfall condition. Based on detailed landslide inventory and rainfall data in the hilly area in Sichuan province, this study analyzed the effects of both rainfall process and environmental factors on the occurrence of landslides. Through analyzing environmental factors, a landslide susceptibility index (LSI) was calculated using multiple layer perceptron (MLP) model to reflect the regional landslide susceptibility. Further, the characteristics of rainfall process and landslides were examined quantitatively with statistical analysis. Finally, a probability model integrating LSI and rainfall process was constructed using logistical regression analysis to assess the landslide hazard. Validation showed satisfactory results, and the inclusion of LSI effectively improved the accuracy of the landslide hazard assessment: Compared with only considering the rainfall process factors, the accuracy of the landslide prediction model both considering the rainfall process and landslide susceptibility is improved by 3%. These results indicate that an integration of susceptibility index and rainfall process is essential in improving the timeliness and accuracy of regional landslide early warning.

Landslide Vulnerability Identification Based on the Geological Condition, GIS Calculation, and Field Validation in the Tropical Area

“…The factors used to classify the area's vulnerability to landslides are slope, lithology, land wetness, relative relief, land cover, and rainfall as supporting data in the study. The method can then be translated into a numerical classification scheme called the landslide hazard evaluation factor (LHEF) [38][39][40][41][42] .…”

Remote Sensing Identification and Hazard Assessment Methods for Spoil Sites

“…In addition, researchers have conducted extensive research on regional geological hazards' assessment and analysis. To this end, emerging remote sensing methods, including aerial remote sensing platforms (Yang et al, 2022;Herng et al, 2019), sensors, UAVs (Yang, 2022), airborne LiDAR (Liang et al, 2021;Xu, 2019Xu, ,2020Xu, ,2022a, in combination with analytic hierarchy process (Li, 2021), random subspace fuzzy rules (Binh et al, 2016), grey relational analysis (Liu, 2022), backward cloud algorithm (Tian and Xiao, 2019), rough-set-based extension evaluation models (Qiao et al, 2020), multi-layer perceptron (MLP) models (Wang, 2020), and precision evaluation methods related to stability (Du, 2018), as well as Bayesian belief networks (John et al, 2021), have been utilized for comprehensive regional assessments.…”

Hazard Assessment of Rainfall–Induced Landslide Considering the Synergistic Effect of Natural Factors and Human Activities

“…This method usually uses various physical models to explore the possibility of landslides occurring under different trigger factors (i.e., rainfall, earthquake) for specific slope structures and constituent materials [17][18][19][20]. For example, Zhuang et al used an infinite-slope model to evaluate the hazard of shallow landslides on the loess plateau under different rainfall conditions based on the decay law of rainfall-caused soil strength change [17]. The physical method does not rely on a large amount of historical landslide data.…”

“…Although the existing methods for landslide hazard assessment have made remarkable achievements in the analysis of impact factors causing landslide disasters and quantitative evaluation models of landslide hazards, some limitations still need to be further solved. On the one hand, most existing methods only use rainfall as a trigger factor, resulting in low accuracy of the assessment results [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]; on the other hand, the current methods usually model hazards in different geographical scenarios as a whole or consider only one-factor variability [28,29], ignoring the impact of multi-factor spatial stratified heterogeneity and spatial correlation, which makes it difficult to explain the generation and evolution process of the assessment results. This paper proposes a method that considers the synergistic effect of multiple factors to solve these issues.…”