DISASTER PREDICTION USING MACHINE LEARNING

Abstract

This study presents an advanced disaster prediction framework that enhances traditional machine learning approaches through an intelligent hybrid mechanism integrating multi-source data processing and adaptive learning techniques. Unlike conventional systems that rely heavily on static historical datasets, the proposed model incorporates heterogeneous inputs such as environmental records, sensor data, and spatio-temporal attributes to capture complex disaster patterns more effectively. The framework combines deep learning-based feature extraction with ensemble learning strategies to improve predictive accuracy and robustness across multiple disaster categories, including floods, earthquakes, and wildfires. To address common challenges such as class imbalance and missing data, the model employs optimized preprocessing techniques, including data imputation and synthetic sample generation, ensuring balanced and reliable training. Furthermore, the integration of anomaly detection mechanisms enables early identification of unusual patterns, supporting real-time disaster forecasting. Experimental analysis demonstrates that the proposed approach significantly outperforms conventional machine learning models in terms of accuracy, generalization, and stability. The system also enhances decision-making capabilities by providing actionable insights for disaster preparedness, resource allocation, and risk mitigation. Overall, this research contributes a scalable, efficient, and data-driven solution for next-generation disaster management systems