Predicting the thickness of shallow landslides in Switzerland using machine learning
Christoph Schaller,
Luuk Dorren,
Massimiliano Schwarz
et al.
Abstract:Abstract. Landslide thickness is a key parameter in various types of models used to simulate landslide susceptibility. In this study, we developed a model providing improved predictions of potential shallow landslide thickness in Switzerland. We tested three machine learning models based on random forests, generalized additive model, and linear regression and compared the results to three existing models that link soil thickness to slope and elevation. The models were calibrated using data from two field inven… Show more
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