A Hybrid Machine Learning Framework Combining Neural Networks and XGBoost for Disaster Prediction and Management

Abstract

Natural disasters such as floods, earthquakes, cyclones, and wildfires cause significant
damage to human life, infrastructure, and the environment. Accurate and timely prediction of
such disasters is essential for minimizing their impact and improving disaster management
strategies. This research proposes a hybrid machine learning framework that combines Neural
Networks and Extreme Gradient Boosting (XGBoost) to enhance the accuracy and efficiency
of disaster prediction and management. The system utilizes historical disaster data,
meteorological parameters, and environmental indicators to train predictive models capable of
identifying potential disaster occurrences. In the proposed framework, neural networks are
used to capture complex nonlinear relationships among environmental and climatic variables,
while XGBoost improves prediction performance by handling structured data efficiently and
reducing overfitting through gradient boosting techniques. By integrating the strengths of
both approaches, the hybrid model provides more reliable predictions compared to traditional
single-model techniques. The system also supports early warning mechanisms that assist
authorities and disaster management agencies in making timely decisions. Experimental
results demonstrate that the hybrid model achieves higher prediction accuracy, improved
recall, and better generalization when compared with conventional machine learning
algorithms. The proposed framework can be applied to various disaster scenarios and
integrated into real-time monitoring systems to support proactive disaster preparedness and
response. This approach contributes to the development of intelligent disaster management
systems that help reduce risks, enhance safety, and improve overall resilience against natural
disasters.