Saving Our Forests: Predicting Wildfires Using Artificial Intelligence

Project Mission: Develop an AI-powered tool that combines weather, vegetation, and terrain data to estimate wildfire risk and support environmental awareness and community preparedness.

Wildfires threaten forests, wildlife, homes, and communities. Weather conditions such as high temperature, strong winds, low humidity, and limited rainfall can increase wildfire risk, but vegetation and terrain also play important roles. This project will explore how these environmental factors can be combined to identify areas with elevated wildfire risk.

Using historical wildfire records, weather data, satellite-based vegetation information, and terrain data, we will develop a machine-learning model using Python. The model will learn patterns associated with past wildfire events and estimate risk under new environmental conditions.

The project will include four major steps: collecting and organizing environmental data, training and testing the AI model, developing an interactive wildfire-risk dashboard, and evaluating model performance using measures such as precision, recall, and area under the ROC curve.

The final project will produce a trained wildfire-risk model, integrated environmental dataset, interactive color-coded risk map, and performance report describing the model's strengths and limitations.

This project combines environmental science, satellite technology, artificial intelligence, programming, and data science to explore how technology can help communities better understand wildfire risk and prepare for potential threats. The tool will estimate wildfire risk; it will not predict the exact time or location of every future wildfire.