Forest Fire Prediction & Alert System
Year: 2025
Type: article
Abstract: Ecosystems, human settlements, and biodiversity are all seriously threatened by forest fires, which iswhy proactive monitoring and early warning systems are essential. With the use of machine learning,vegetation analysis, and real-time weather data, this project offers a forest fire prediction system specificallydesigned for Tamil Nadu's forested areas. The system is made up of a Flask-based backend that uses forestcoordinates to retrieve real-time weather data (temperature, humidity, and wind speed) from theOpenWeatherMap API. In order to evaluate dryness levels, it also takes into account the distribution ofvegetation types, which are taken from pre-established forest datasets. Using environmental factors, aRandom Forest-based machine learning model forecasts the risk of fire. If the risk is high, automated emailalerts are sent to the appropriate authorities. The frontend, built with React and TailwindCSS, displays TamilNadu's forests as interactive cards, providing weather details, vegetation analysis, and fire risk predictions.Power BI integration ensures effective visualization of weather trends and vegetation distribution, aiding indecision-making. The system's RESTful API delivers real-time updates, ensuring seamless data flowbetween components. By integrating fire prediction with real-time monitoring, this project enhances forestmanagement strategies and disaster preparedness, minimizing ecological and economic damage fromwildfires.
Author: Maheswari M, Mrs.Mohana G
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: green
OpenAlex ID: W6893298679
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