Machine learning for the definition of landslide alert models: a case study in Campania region, Italy
This preliminary study aims to demonstrate that machine learning (ML) techniques can be used to analyze monitoring data to select the most relevant variables for triggering shallow rainfall-induced landslides at a regional scale. Assessing the occurrence of shallow rainfall-induced landslides is crucial for engaging in effective short-term and long-term risk protection actions. The models developed in this preliminary study were tested in one of the alert zones defined by civil protection for the management of geo-hydrological risk in the Campania region, Italy.
Conclusions from the study indicate that the Likelihood-Fuzzy Analysis (LFA) method, an ML method used for predicting the occurrence of landslides, demonstrated that it is a valid support for identifying the most relevant variables to trigger shallow rainfall-induced landslides and for clearly representing their relations with the predicted outcome, thanks to the model interpretability. Moreover, the good performance of models found in the present work and the possibility of producing robust and confidence-based results confirm that the LFA method can be proficiently applied, in place of more classical ML approaches, for building rainfall-induced landslide alert models.
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