Space-time prediction of rainfall-induced shallow landslides through Artificial Neural Networks in comparison with the SLIP model

Abstract

Rainfall-induced shallow landslides are expected to increase due to more intense precipitation linked to climate change. This study aims to develop an effective pixel-based tool for the space-time prediction of soil slips by combining a FeedForward Neural Network (FFN) with insights from the physically-based model SLIP (Shallow Landslide Instability Prediction). The FFN model was developed based on past events in four towns of the Emilia Apennines (Italy) from 2004 to 2014 under varying rainfall conditions. Among the key aspects analysed were the inclusion of both landslide and non-landslide days, the evaluation of two different cumulative rainfall periods (10 and 30 days), and various technical elements related to machine learning, including training approach, network topology, and activation function. A 2:1 imbalance in non-landslide/landslide pixels was implemented to enhance prediction performance. Prediction accuracy was measured using the Quality Combined Index (QCI), which combines AUROC, AUPRC, and F1-score. The best FFN model achieved a QCI of 0.85, accurately predicting non-landslides and minimizing false alarms. A comparison with SLIP showed that SLIP better captured the progressive destabilization in areas nearing instability, while the FFN provided a clearer distinction between stable and unstable zones. A successful blind prediction was demonstrated for a landslide in Compiano (November 2019), validating the model's applicability. SLIP also contributed to understanding the initial soil saturation and rainfall conditions, highlighting its potential to enhance FFN predictions in different meteorological scenarios. Although the developed pixel-based model could be utilized as is, further research is needed to enhance its application for early warning purposes in varying meteorological conditions.