Project Title: Natural Disaster Prediction Models

Objective:

To develop machine learning models that can predict natural disasters—such as earthquakes, floods, wildfires, or hurricanes—using historical and real-time environmental data, thereby enabling early warning systems and proactive disaster management.

Key Components:

Data Collection:

Gathers data from sources like:

Seismic sensors, satellite imagery, weather stations, and hydrological databases.

Includes parameters such as rainfall, temperature, humidity, soil moisture, wind speed, and seismic activity.

Disaster-Specific Models:

Earthquake Prediction: Analyzes seismic wave patterns and foreshocks using time series models or anomaly detection.

Flood Prediction: Uses rainfall, river levels, and terrain data with models like LSTM, Random Forest, or XGBoost.

Wildfire Forecasting: Incorporates weather, vegetation index (NDVI), and human activity data with classification models.

Hurricane Tracking: Applies deep learning to satellite imagery and atmospheric data for storm path forecasting.

Preprocessing & Feature Engineering:

Converts raw geospatial and time-series data into structured features.

Applies spatial-temporal analysis, image preprocessing (for satellite data), and outlier handling.

Machine Learning & Deep Learning Models:

Includes models such as:

Convolutional Neural Networks (CNNs) for image-based analysis.

Recurrent Neural Networks (RNNs/LSTMs) for temporal predictions.

Gradient boosting and SVM for structured data classification.

Risk Scoring & Alert System:

Generates risk levels for regions based on prediction confidence.

Triggers automated alerts and notifications for authorities and the public.

Visualization & Mapping:

Interactive dashboards and GIS maps show risk zones, prediction timelines, and live sensor data.

Supports emergency response planning and resource allocation.

Outcomes:

Enables early warning systems for faster disaster response.

Reduces human and economic losses through data-driven risk mitigation.

Aids government and NGOs in preparedness and planning.

Demonstrates the power of AI in climate resilience and public safety.