Wildfire Spread Prediction Through Remote Sensing and UAV Imagery-Driven Machine Learning Models

Mohammad Reza Mohebbi, Elian Wira Sena, Mario Döller, Julian Klinger

Abstract: Wildfires not only pose a significant threat to human life and property but also have far-reaching impacts on communities and ecosystems. Effective prevention and mitigation strategies rely on accurate prediction of the path of these fires. This paper proposes the utilization of data obtained from Unmanned Aerial Vehicles (UAVs) to develop predictive models for fire spread. A comprehensive dataset is presented that includes key environmental variables that have been meticulously captured using these advanced technologies. The dataset comprises images from which essential features for predicting fire spread have been extracted. The method detailed in this article has been used to identify and incorporate crucial factors such as plant density, wind direction and speed, humidity, and geographical features. These key factors are then used to predict the spread of fires using Machine Learning (ML) techniques. After thorough study and comparison, AdaBoost and Random Forest (RF) demonstrate superior predictive capabilities. Evaluation metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE) confirm the high accuracy and reliability of the proposed approach, achieving R-squared ($\mathrm{R}^{2}$) values above 0.98. By combining advanced technological tools with analytical methodologies, this approach has the potential to enhance fire suppression and management, safeguarding lives and assets.

External IDs:dblp:conf/icarcv/MohebbiSDK24