An interpretable and transferable model for shallow landslides detachment combining spatial Poisson point processes and generalized additive models

TL;DR

This paper introduces an interpretable, transferable probabilistic model for shallow landslides using spatial Poisson point processes and generalized additive models, with a novel predictor selection workflow based on Random Forests.

Contribution

It combines spatial Poisson processes with GAMs for landslide prediction and proposes a new predictor selection workflow to enhance interpretability and transferability.

Findings

Model achieves good predictive performance.

Identifies activating and stabilizing geophysical factors.

Quantifies uncertainty with bootstrap methods.

Abstract

Less than 10 meters deep, shallow landslides are rapidly moving and strongly dangerous slides. In the present work, the probabilistic distribution of the landslide detachment points within a valley is modelled as a spatial Poisson point process, whose intensity depends on geophysical predictors according to a generalized additive model. Modelling the intensity with a generalized additive model jointly allows to obtain good predictive performance and to preserve the interpretability of the effects of the geophysical predictors on the intensity of the process. We propose a novel workflow, based on Random Forests, to select the geophysical predictors entering the model for the intensity. In this context, the statistically significant effects are interpreted as activating or stabilizing factors for landslide detachment. In order to guarantee the transferability of the resulting model, training, validation, and test of the algorithm are performed on mutually disjoint valleys in the Alps of Lombardy (Italy). Finally, the uncertainty around the estimated intensity of the process is quantified via semiparametric bootstrap.