Geo-Forensic Analysis of Levee and Slope Failures Using Machine Learning

Levee and slope failures are among the most critical soil-related hazards, often driven by complex interactions between soil properties, hydrological pressures, and structural conditions. Traditional geotechnical investigations provide valuable insights into these processes but face limitations in capturing the nonlinear and dynamic behavior of soils under stress. Advances in soil science, remote sensing, and computational modeling now enable the integration of multi-source datasets to better understand and predict soil-driven failures.This study presents a geo-forensic framework that applies machine learning (ML) to analyze soil stability, classify failure mechanisms, and assess levee performance under varying hydrological and geological conditions. By incorporating soil parameters (e.g., shear strength, permeability, and moisture content), pore water pressures, and external hydrological loads, the framework improves both post-failure forensic analysis and pre-failure risk assessment. Remote sensing and monitoring data are further integrated to enhance spatial understanding of soil deformation and failure progression.Case studies demonstrate that ensemble ML models such as Random Forests and Support Vector Machines achieve high accuracy in predicting slope instability and identifying soil-related precursors of levee failures. Results confirm that ML-based soil analysis not only supports forensic classification but also enables proactive hazard mitigation. By bridging soil mechanics, hydrology, and data-driven modeling, this research underscores the central role of soil science in disaster resilience, offering a pathway to improved monitoring, design, and management of levees and slopes under changing environmental conditions.