Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold

• Published in: Geomorphology (Amsterdam, Netherlands) Vol. 408; p. 108236
• Main Authors: Huang, Faming, Chen, Jiawu, Liu, Weiping, Huang, Jinsong, Hong, Haoyuan, Chen, Wei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2022
• Subjects: China; Critical rainfall threshold; Effective rainfall intensity; geomorphology; Landslide hazard warning; Landslide susceptibility prediction; landslides; prediction; probability; rain; rain intensity; rainfall duration; Rainfall-induced landslides; Random forest; regression analysis; support vector machines; China; Landslide hazard warning; Critical rainfall threshold; Random forest; Rainfall-induced landslides; Landslide susceptibility prediction; Effective rainfall intensity
• DOI: 10.1016/j.geomorph.2022.108236
• ISSN: 0169-555X

Rainfall-induced landslide hazard warning, which refers to the prediction of the spatial-temporal probability of landslide occurrence in a certain area under the conditions of continuous rainfall processes, can be established based on landslide susceptibility mapping and critical rainfall threshold calculations. However, it is difficult to determine appropriate machine learning models for mapping landslide susceptibility. Additionally, it is significant to consider the influences of early effective rainfall on landslide instability in the critical rainfall threshold methods. Furthermore, the uncertainties of the critical rainfall threshold values generated by different calculation methods have not been well explored. To overcome these three drawbacks, first, frequency ratio analysis-based logistic regression (LR), support vector machine (SVM) and random forest (RF) models are adopted to predict landslide susceptibility for machine learning model comparison. Second, three different types of critical rainfall threshold methods, namely, cumulative effective rainfall-duration (EE-D), effective rainfall intensity-duration (EI-D) and cumulative effective rainfall-effective rainfall intensity (EE-EI) models, are proposed to calculate the temporal probabilities of landslide occurrence under rainfall conditions based on the concept of effective rainfall. The accuracies and uncertainties of these three critical rainfall threshold methods are discussed. Finally, the landslide susceptibility maps and the critical rainfall threshold values are coupled to predict the rainfall-induced landslide hazards. Xunwu County in China is selected as the study area, and several rainfall-induced landslides are used as the test samples of the proposed landslide hazard warning model. The results show that the RF model has remarkably higher susceptibility prediction accuracy than the SVM and LR models, and the prediction performance of the temporal probabilities of landslide occurrence using the EI-D values are higher than those of EE-D and EE-EI values. Furthermore, rainfall-induced landslide hazard warning is effectively implemented based on the coupling of the susceptibility map and EI-D model. [Display omitted] •Rainfall-induced landslide hazard warning is examined by landslide susceptibility mapping and critical rainfall threshold.•Various machine learning models are compared for predicting landslide susceptibility.•Uncertainties of different critical rainfall threshold models for landslide hazard warning are explored.•Effective rainfall intensity-duration threshold model has the highest accuracy than other models.