ABSTRACT
This project presents an advanced web-based platform for predicting and analyzing five major natural disasters with 5 different languages for floods, forest fires, hurricanes, earthquakes, and droughts. Developed using Python's Flask framework, the system integrates multiple machine learning algorithms to provide accurate risk assessments and early warnings. The backend utilizes MongoDB for secure user authentication and storage of disaster-related datasets, while historical data is processed from CSV files to train and validate the prediction models.
For flood prediction, the system employs an ensemble approach combining Decision Tree, Random Forest, KNN, and XGBoost algorithms to analyze parameters like monsoon intensity, drainage capacity, and river management. Forest fire predictions are generated through a Random Forest classifier that processes temperature, humidity, and oxygen level data. Hurricane intensity is estimated using Random Forest regression to predict wind speeds based on atmospheric pressure and geographical coordinates.
Earthquake risk assessment is performed using Random Forest regression that considers seismic magnitude, depth, and geological features. Drought prediction utilizes a Random Forest classifier trained on geographical coordinates, elevation data, and climatic conditions. Each model was carefully selected and trained to optimize prediction accuracy for its specific disaster type.
The web interface features a responsive dashboard that displays real-time predictions, interactive maps for geographical visualization, and an alert system that categorizes risks into different severity levels. Users can register accounts to access personalized features and save prediction histories. The system implements secure authentication with password hashing for data protection.
Key functionalities include dynamic data visualization through charts and maps, a notification system for high-risk events, and comprehensive reporting tools. The modular architecture allows for easy expansion to include additional disaster types in the future. By combining machine learning with user-friendly interfaces, this system aims to enhance disaster preparedness and mitigation efforts.
The project demonstrates effective integration of multiple technologies including Flask for web development, MongoDB for data persistence, scikit-learn for machine learning implementations, and various Python libraries for data processing and visualization. This comprehensive approach results in a robust platform that can assist both individuals and authorities in making informed decisions during potential disaster situations.


OBJECTIVES
1. Develop Accurate Multi-Hazard Prediction Models
o Implement ensemble machine learning techniques (Random Forest, XGBoost, KNN) for flood, fire, hurricane, earthquake, and drought prediction.
o Train models on historical and real-time environmental data to improve forecast reliability.
o Achieve >80% accuracy in risk classification through cross-validation and hyperparameter tuning.
2. Create an Integrated Web-Based Platform
o Design a unified Flask-powered interface for all disaster types with MongoDB backend.
o Enable seamless switching between prediction modules for different hazards.
o Ensure responsive design for desktop.
3. Implement Data Processing
o Connect to datasets for dynamic updates.
o Develop automated pipelines to refresh predictions based on new data.
o Trigger instant alerts when risk thresholds are exceeded.
4. Enhance Decision-Making Through Visualization
o Generate interactive maps with Leaflet/Mapbox to display risk zones geographically.
o Design intuitive dashboards with charts showing prediction confidence levels.
o Incorporate historical trend analysis for comparative risk assessment.
5. Ensure Scalability and Accessibility
o Build modular architecture to add new disaster types or regions in future.
o Optimize system performance to handle 10,000+ concurrent users during crises.
o Include multi-language support and simplified UI for diverse user groups.