Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

Abstract

Landslide represents an increasing menace causing huge casualties and economic losses, and rainfall is a predominant factor inducing landslides. Landslide susceptibility assessment (LSA) is a commonly used and effective method to prevent landslide risk, however, the LSA does not analyze the impact of the rainfall on landslides which is significant and non-negligible. Therefore, the spatiotemporal LSA considering the inducing effect of rainfall is proposed to improve accuracy and applicability. In this study, the influencing factors are selected using the chi-square test, out-of-bag error and multicollinearity test. The spatial LSA are thus obtained using the random forest (RF) model, deep belief networks model and support vector machine, and compared using receiver operating characteristic curve and seed cell area index to determine the optimal assessment result. According to the heavy rainfall characteristics in the study area, the rainfall period is divided into four stages, and the effective rainfall model is employed to generate the rainfall impact (RI) maps of the four stages. The spatiotemporal LSAs are obtained by coupling the optimal spatial LSA and various RI maps and verified using the landslide warning map. The results demonstrate that the optimal spatiotemporal LSA is obtained using the spatial LSA of the RF model and temporal LSA of the rainfall data in the peak stage. It can predict the area where rainfall-induced landslides are likely to occur and prevent landslide risk.

Landslide Susceptibility Assessment by Machine Learning and Frequency Ratio Methods Using XRAIN Radar-Acquired Rainfall Data

“…The ANN method, widely regarded as one of the most appropriate models for LSM production [15,23,74,75], scored an AUROC of 92.47% in this study, second in the list, and exhibiting an appropriate LSI zone distribution in the resultant LSM and steady increase in landslide density per LSI. Being able to work well with large datasets and adaptable to many different tasks, neural network models may still be regarded as one of the leading ML models for many kinds of assessment, as was demonstrated in this study.…”

“…Among many different ML methods, one of the most recommended for landslide susceptibility assessment is the random forest (RF) method, as also pointed out by Goetz et al [12], Chen et al [22], Youssef and Pourghasemi [13], Li et al [23], Xia et al [24], Imtiaz et al [25], Huang et al [19], and Daviran et al [26].…”

Post-Landslide Liquefaction Analysis: A Case Study in the Kupang Regency Area, Indonesia

“…Predicting landslide movements requires considering the intricate interplay among various elements, with models like GA-LSSVM being suggested for forecasting landslide progression [21]. Studies have delved into how lithology, slope gradients, orientations, peak ground accelerations, and proximity to faults influence the occurrence of co-seismic landslides [14]. Furthermore, the utility of bivariate statistical methods in correlating landslide inventories with parameters influencing landslides for susceptibility analysis has been documented [22].…”

“…Hungr et al [36] stressed the necessity of adopting a multidisciplinary approach to devise holistic solutions for mitigating landslide risks and crafting prevention mechanisms Li et al [14], Bucknam et al [37], and Glade et al [38] further emphasized the multidisciplinary strategy by discussing the application of empirical models to refine rainfall thresholds that trigger landslides.…”

“…Landslides occur when this balance is disturbed, leading to shear failure along a plane within the soil or rock mass. This failure is often a complex interplay between the material's inherent shear strength and the external stresses applied to it, including those from natural or human-induced changes in the environment [8][9][10][11][12][13][14][15].…”