Abstract
This study presents an advanced machine learning framework for predicting landslides in Moio della Civitella, Italy, utilizing a comprehensive dataset from 2015-2019. Integrating Self-Supervised Learning for Anomaly Detection, Ensemble Methods, Long Short-Term Memory networks (LSTM) for Time-Series Forecasting, and Gradient Boosting Machines for Feature Importance, the research identifies critical temporal and seasonal patterns in landslide occurrences. Visual tools like Time-Series Plots and Anomaly Heatmaps highlight significant deviations and high-preparedness periods, particularly during December to February. Validation through precision and recall, alongside ROC curves, demonstrates improved prediction accuracy. Despite inherent uncertainties and dependencies on data quality, the approach significantly enhances the predictability of landslides, offering a robust tool for early warning systems and risk management strategies, thereby aiming to mitigate the human and economic toll of such natural disasters.