Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning in image classification

Disaster prediction and management are crucial for mitigating the impacts of natural and man-made disasters, and machine learning techniques have shown significant promise in this area. This project introduces Neural-XGBoost, a hybrid approach for disaster prediction and management using machine learning. The model integrates the power of four distinct algorithms: XGBoost, Random Forest, Neural Networks, and a hybrid Neural-XGBoost model, combining the strengths of each to enhance prediction accuracy. The XGBoost model is utilized for its superior ability to handle large datasets and feature selection, while the Random Forest model improves robustness in high-dimensional data. The Neural Network model is implemented to capture complex non-linear relationships in the data, while the hybrid Neural-XGBoost model synergistically combines deep learning with the performance of gradient-boosting, improving disaster prediction outcomes. By leveraging these techniques, the system aims to provide a scalable and accurate solution for disaster management, offering real-time forecasting that can assist in proactive decision-making. The models are developed and evaluated using Python and libraries such as scikit-learn and Keras. The research highlights the potential of combining traditional machine learning models with deep learning to improve disaster prediction systems and provide actionable insights for timely intervention and resource management in disaster-prone areas.

Keywords: Disaster Prediction, Machine Learning, Neural Networks, XGBoost, Random Forest, Hybrid Model, Deep Learning, Disaster Management, Real-Time Forecasting, Python, Anomaly Detection, Predictive Modeling.