Landslide predictions through combined rainfall threshold models
Citation
Guzzetti, F., Gariano, S.L., Peruccacci, S., Brunetti, M.T., Melillo, M., Rossi, M., Luciani, S. (2024). Landslide predictions through combined rainfall threshold models. Landslides, 22: 137-157. Link to paper
Abstract
Despite the extensive literature on the definition and use of rainfall thresholds, little attention has been given to examining and comparing the mathematical methods that can be used to define thresholds as lower bounds of clouds of empirical rainfall conditions known to have triggered landslides. When multiple thresholds are available, it is unclear how to combine them. The study tested and compared four mathematical methods to define event cumulated rainfall—rainfall duration (ED) thresholds using 2259 measurements of rainfall duration (D, in hours) and cumulated rainfall (E, in mm) that resulted in mostly shallow landslides in Italy between January 2002 and December 2012. The methods cover a broad spectrum of data driven approaches, including a frequentist least square method, a frequentist quantile regression method, a Bayesian quantile regression method, and a machine-learning symbolic regression method. The methods were applied and compared for three non-exceedance probability levels, p = 0.01, 0.05, 0.10, and a voting strategy to combine the predictions into a single, dichotomous—i.e. 'sharp'—non-probabilistic landslide prediction was proposed.