Secular analysis of landslides and prediction of their occurrence by Hierarchical Bayesian Model

Abstract

In recent years, concerns have grown over the increasing severity of landslide disasters due to climate change, making it essential to understand their occurrence trends in detail for each region. In this study, we analyzed landslide occurrence data from 1983 to 2022 using a hierarchical Bayesian model, with maximum one-hour rainfall and elapsed years as explanatory variables. To account for the characteristics of landslide data with many zeros, we employed a zero-inflated negative binomial distribution, which statistically distinguishes between structural and incidental zeros. The statistical analysis revealed increasing trends in landslide occurrences in seven prefectures, enabling a quantitative understanding of regional differences in risk. In Iwate Prefecture, which showed the most significant increase, the proportion of years with non-zero landslide occurrences (non-zero probability) was estimated at 44.2% in 1983 and 80.8% in 2050. The model developed in this study can be applied to future risk forecasting based on rainfall changes and is expected to contribute to region-specific risk assessment.