ADVANCED MACHINE LEARNING STRATEGIES FOR LANDSLIDE DETECTION - read online

Aug 3, 2026

Uploaded by Theodore

This study presents an advanced machine learning framework for landslide prediction in Moio della Civitella, Italy, using a comprehensive dataset spanning 2015–2019. Self-supervised learning drives anomaly detection, while ensemble methods support uncertainty quantification. Long short-term memory (LSTM) is used for time-series forecasting, and gradient boosting machines provide feature importance to identify key temporal and seasonal drivers. Time-series plots and anomaly heatmaps reveal strong deviations and elevated preparedness periods, especially from December to February. Model validation with precision, recall, and ROC curves improves predictive accuracy despite data-quality uncertainties.

LanguageEnglish

CategoryResearch & Report

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Abstract

Introduction

Data and Methods

IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium | 979-8-3503-6032-5/24/$31.00 ©2024 IEEE | DOI: 10. 1 109/IGARSS53475.2024. 10641104 ADVANCED MACHINE LEARNING STRATEGIES FOR LANDSLIDE DETECTION Mohammad Amin Khalili1, Behzad Voosoghi2, Domenico Calcaterra1, Amirbahador Kouchakkapourchali3, Chiara Di Muro1, Sadegh Madadi4, Rita Tufano1, Diego Di Martire1 1 : University of Naples Federico II, Naples, Campania, Italy 3 : University of Tehran, Tehran, Iran 2 : K. N. Toosi University of Technology, Tehran, Iran 4 : Kharazmi University, Tehran, Iran 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. Index Terms— Landslide Prediction, Anomaly Detection, Time-Series Forecasting, Self-Supervised Learning, Long Short-Term Memory (LSTM) Networks, InSAR, COSMO-SkyMed (CSK) Satellite Imagery 1. INTRODUCTION Landslides constitute a pervasive and devastating natural hazard affecting millions worldwide, with significant impactson life, infrastructure, and economies [1], [2] . Italy, with its unique and varied geology, experiences a high frequency of these events, making landslides a matter of national concern [3] . In particular, the southern region, including areas like Moio della Civitella, is frequently affected due to its steep terrains and intense seasonal rains. These landslides not only cause immediate destruction but also lead to long-term socioeconomic challenges, underscoring the need for effective prediction and mitigation strategies [4] . The primary problem in effectively managing landslide hazards is the complexity of predicting when and where they will occur. Current methods often provide inadequate warning, leading to unnecessary evacuations or, conversely, significant damage and loss of life due to missed or late detections. The challenge lies in accurately identifying potential landslide events in advance, considering the myriad of contributing factors and the inherent uncertainty in such natural phenomena [5] . To address these challenges, this study adopts a comprehensive machine learning approach, integrating SelfSupervised Learning for Anomaly Detection [6], Ensemble Methods for Uncertainty Quantification [7], Time-Series Forecasting with LSTM networks [4], and Feature Importance Analysis using Gradient Boosting Machines [8] . This methodology harnesses the power of advanced algorithms to learn from environmental data, predict potential landslide incidents, and understand the importance of various predisposing factors. The aim is to detect unusual patterns and predict future events with greater accuracy and reliability, thereby enabling timely interventions. In conclusion, this work represents a significant advancement in the field of landslide hazard management. By leveraging cutting-edge machine learning techniques and a rich dataset, the study provides a robust framework for predicting landslides with improved accuracy and reliability. The findings and predictive models developed herein hold the potenti

FAQs

What landslide dataset and study area are used in the framework?

The study uses COSMO-SkyMed (CSK) satellite imagery and related environmental data for Moio della Civitella, Italy, covering 2015–2019, including deformation information derived from CPT-TPC and monthly rainfall records.

Which machine learning methods are integrated for prediction and interpretation?

The framework combines self-supervised learning for anomaly detection, ensemble methods for uncertainty quantification, LSTM for time-series forecasting, and gradient boosting machines to assess feature importance.

How is model performance validated and what visual evidence is provided?

Performance is evaluated using precision, recall, and ROC curves, while time-series plots and anomaly heatmaps highlight significant deviations and high-preparedness periods, particularly during December to February.

Aug 3, 2026

Uploaded by Theodore