A probabilistic early warning model of rainfall-induced landslides accounting for multiple factors and correlation structures

The frequency and intensity of extreme rainfall events have been increasing, resulting in an escalating number of landslides. The rainfall factors that induce landslides are frequently multidimensional and possess intrinsic correlations. However, the existing probability analysis models for rainfall-induced landslides rarely consider multiple factors and their correlations, resulting in an inability to accurately reflect the effect of rainfall on landslides. To address this issue, this study proposes a landslide probability analysis model that considers multiple rainfall factors and their correlations. The paper first introduces the relevant theories used for modeling. Then, it elaborates on the method and steps of building the model in detail. Finally, it illustrates and demonstrates the effectiveness of the proposed model through an example of rainfall-Induced Landslides. The results show that: (1) Different rainfall factors show different distribution characteristics. (2) The D-vine Copula model can offer a superior degree of accuracy in the description of the structural characteristics of multidimensional rainfall factors. (3) This model has the capacity to ascertain the conditional probability and exceedance probability of landslides occurring under various conditions of rainfall. (4) The probability of landslide occurrence is significantly underestimated when the correlation between multiple rainfall factors is not taken into consideration. In summary, this method enhances the precision of probability analysis of rainfall-induced landslides.