Application of machine learning and numerical simulation for monitoring and early warning systems of landslides and rockfalls in geohazard-prone regions

This paper proposes an integrated framework for ML and numerical simulation-based monitoring and early Warning Systems (EWS) of landslides and rockfalls in geohazard-prone areas. The proposed strategy will efficiently be monitoring a geohazard risks and forecast slope failures by integrating physics-based simulations with supervised ML techniques. The framework does continuous evaluations of the stability of slopes utilizing data from many sources, such as field sensors, remote sensing and historical geohazard records. Key results indicate that with accuracy, precision, recall and F1-score at 99.50%, 99.50%, 96.58%, and 97.99%, correspondingly, the proposed RF-SVM-PCA model gives better results than other models. Compared with previous RF-XGB-GA, XGB-LGBM and RF, the proposed model provides less false alarm and false negative forecasts and more accurate prediction. The cloud-based system has demonstrated consistently good performance for latency, throughput, and scalability with notable improvements under cloud service metrics. Combining the advantages of ML and numerical simulations, the proposed framework enhances its predictive accuracy, delivers timely alarms and thus assists the authorities in planning interventions by reducing a danger to human life and infrastructure. Overall, a framework guarantees very good scalability and adaptability in implementation across diverse geographic zones and represents a significant step ahead in EWS. The proposed framework is validated on two benchmark datasets with more than 15,000 data instances covering varied rainfall, slope and seismic conditions. Implementation of the system on a cloud-integrated workstation-a computer with the configuration Intel i5-12400 and 8 GB RAM-and field testing in simulated hilly terrain showed stable latency below 150 ms and reduced false alarms by almost 20% compared to baseline models, confirming the applicability of the system to the real world.