Disaster Prediction and Early Warning Using Machine Learning

Author

Dr. D. Richard, Shivadhanush S

Abstract

 Disaster early-warning systems increasingly combine environmental sensing, remote sensing, historical records, and machine-learning models.[1]  This review-based paper examines how Extreme Gradient Boosting (XGBoost) can support disaster prediction and early warning across flood, landslide, wildfire, and earthquake-related applications. The reviewed literature supports XGBoost most clearly for structured, sensor-driven tasks such as short-horizon flood-stage forecasting, landslide alert-severity prediction, and multi-hazard classification.[2–4]  A flood-alert study used five-minute stage forecasts and continuous detection, while a landslide study predicted alert severity ten minutes ahead.[2, 3]  A hybrid Neural-XGBoost study reported strong classification performance on a dataset containing wildfires, floods, and earthquakes, but that result should be interpreted as dataset-specific feasibility rather than universal operational validation.[4]  The literature also establishes an important boundary for earthquakes: machine learning is more defensible for probabilistic hazard estimation, rapid signal detection, and early warning than for deterministic prediction of an individual event.[5]  The paper proposes a hazard-specific, time-aware, leakage-controlled XGBoost workflow that evaluates not only accuracy but also warning lead time, missed events, false alarms, calibration, robustness, and explanation quality. The central conclusion is that XGBoost should be treated as one component of an end-to-end warning system, not as a universal disaster predictor.

Keywords

Disaster prediction; Early warning systems; Machine learning; Extreme Gradient Boosting (XGBoost); Flood forecasting; Landslide monitoring; Wildfire detection

Full Text:

Download Paper PDF

References

• AI-Driven Disaster Prediction and Early Warning Systems: A Systematic Literature Review. Review of machine learning, deep learning, IoT, remote sensing, and early-warning applications across hazards.
• Early Natural Disaster Prediction Using Machine Learning: A Comprehensive Review. Review of supervised, unsupervised, deep-learning, ensemble, IoT, and explainable-AI approaches.
• Application of Machine Learning in Early Warning System of Geotechnical Disaster: A Systematic and Comprehensive Review. Systematic review of machine learning for geotechnical-disaster prediction and warning.
• Data-Driven Flood Alert System (FAS) Using Extreme Gradient Boosting (XGBoost) to Forecast Flood Stages. XGBoost flood-stage forecasting at five-minute intervals and continuous alert generation.
• Development of an Ensemble Gradient Boosting Algorithm for Generating Alerts About Impending Soil Movements. SVM-XGBoost ensemble for ten-minute-ahead landslide alert-severity prediction.
• Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning. Neural feature extraction combined with XGBoost classification for wildfire, flood, and earthquake classes.
• Utilizing Explainable AI Methodologies: LIME and SHAP, for the Classification of Natural Disasters Through Machine Learning Algorithms. Explainability and XGBoost-based disaster classification.
• Machine Learning for Earthquake Prediction: Techniques for Seismic Forecasting and Disaster Preparedness. Distinction between deterministic earthquake prediction, probabilistic forecasting, rapid detection, and early warning.
• Disaster Prediction Using Appropriate Machine Learning Techniques. Overview of machine-learning techniques used for natural-disaster prediction.
• Machine Learning in Emergency Management: Analysis and Methods. Machine-learning applications spanning forecasting, early detection, warning, monitoring, damage assessment, and response.