Description:

The Wildfire Risk Prediction Tool is a data science-powered web platform designed to predict the likelihood of wildfires in a given region.
It leverages historical wildfire data, weather forecasts, vegetation indexes, and satellite imagery to assess real-time fire risks.
The tool is intended for forest departments, emergency services, and environmental organizations to take proactive measures, issue warnings, and reduce disaster impact.

Key Features:

1. Real-Time Risk Index – Displays wildfire risk levels (Low, Medium, High, Critical) for selected regions.

2. Satellite & Weather Data Integration – Pulls live weather conditions (temperature, humidity, wind speed) and vegetation dryness data.

3. Historical Pattern Analysis – Identifies recurring fire-prone areas based on past incidents.

4. Interactive Heatmaps – Visualizes high-risk zones using geospatial mapping tools.

5. Alert & Notification System – Sends email/SMS alerts when risk levels exceed a threshold.

6. Cause Prediction Insights – Analyzes possible ignition sources such as human activity or lightning.

7. Impact Forecasting – Estimates potential spread area if a fire starts.

8. Downloadable Reports – Generates detailed PDF reports for authorities and NGOs.

Technology Stack:

• Backend: Node.js / Java / PHP (for data processing, API integration, and model execution)

• Frontend: HTML, CSS, Bootstrap, JavaScript (for risk visualization and dashboards)

• Database: MySQL / PostgreSQL / MongoDB (stores historical fire data, risk scores)

• Data Science Layer: Python (Pandas, NumPy, Scikit-learn, XGBoost for prediction modeling)

• APIs & Data Sources: NASA FIRMS (Fire Information for Resource Management System), NOAA weather API, Sentinel-2 satellite imagery

• Visualization: Leaflet.js or Mapbox for maps, Chart.js for risk trend graphs

Example Use Case:

• The system collects weather data showing high temperatures, low humidity, and strong winds in California forest areas.

• Satellite vegetation index (NDVI) shows dry biomass in the region.

• The prediction model assigns a “High Risk” score for the next 48 hours.

• The dashboard highlights affected zones in orange/red, and an alert is sent to local fire departments to deploy monitoring teams.