Project Title: Forest Fire Prediction

Objective:

To develop predictive models that can forecast the occurrence and spread of forest fires using environmental and historical data, enabling early warnings and efficient resource allocation for wildfire prevention and response.

Key Components:

Data Collection:

Gathers data from multiple sources, including:

Meteorological data (temperature, humidity, wind speed, rainfall)

Satellite imagery (to detect fire outbreaks and vegetation health)

Historical fire data (locations, dates, severity)

Topographical data (elevation, slope, vegetation types)

Air quality data (PM2.5, CO levels)

Data Preprocessing:

Cleans and normalizes data, handling missing values and outliers.

Performs feature engineering to create useful variables (e.g., drought index, fire danger index).

Converts satellite imagery into meaningful features (e.g., vegetation index like NDVI).

Exploratory Data Analysis (EDA):

Visualizes patterns between environmental factors (e.g., temperature, humidity) and fire occurrences.

Identifies correlations between seasonality, weather patterns, and fire outbreaks.

Predictive Modeling:

Uses machine learning models like:

Random Forests, Gradient Boosting, XGBoost, and Logistic Regression to classify areas of high fire risk.

Time series forecasting (ARIMA, LSTM) to predict future fire-prone periods based on historical data.

Clustering techniques (K-means) to identify patterns of recurring fire hotspots.

Fire Spread Simulation:

Simulates the spread of fire based on factors like wind, terrain, and fuel types using cellular automata models or spatial models.

Predicts the affected area, allowing for real-time predictions during an ongoing fire event.

Geospatial Mapping & Visualization:

Visualizes fire risk areas through heatmaps and GIS-based mapping.

Shows fire progression, risk zones, and evacuation routes.

Real-time monitoring and alert systems for fire-prone regions.

Early Warning System:

Develops a real-time alert system to notify authorities and residents about imminent fire risks.

Integrates environmental data feeds to trigger automated warnings.

Outcomes:

Helps prevent wildfires by identifying high-risk areas in advance.

Supports timely evacuations and resource allocation in fire-prone regions.

Provides policy recommendations for managing forest fire risks.

Reduces economic loss and environmental damage through proactive interventions.