Machine Learning for Forest Fire Prediction: A Case Study in North Algeria

Abstract

Wildland fires are the most common peril for forests due to climate change. Furthermore, it is an uncontrollable disaster and poses a great deal of threat to human health and ecosystems. In Algeria, almost 40,000 hectares are burned each year, approximately 1% of all existing woodlands of the country. In this work, the forest fire event prediction is highlighted using machine learning. The study utilized data sets from several sources, including fire data obtained from the fire information system for resource management by NASA (FIRMS) and climate data accessed from the NASA energy project API, derived from the MODIS satellite (NASA forecasting of energy resources around the world). Fire data from NASA provides real-time information, spanning from 2000 to 2020. The methodology process of creating the prediction system involved collecting the data, preprocessing the data, finding the best models, training and testing the models, and evaluating them for validation. The machine learning model was trained and validated using 70% and 30% of the set features with a performance accuracy of up to 86%. Upon completion, we deployed our selected machine learning model to create a Web platform enables different end users to check possible future forest fires by select a geographical area on a world map. The objective of our machine learning model is to analyze the weather data of the selecting area on the map in real time and predict whether a fire will occur or not. This prediction system will enhance early detection, allowing prompt response measures to be implemented, reducing the risk of uncontrolled wildfires and safeguarding ecosystems and communities.