A Neural Network Approach to Forest Fire Burned Area Prediction

Abstract

Worldwide ecosystems and economies are seriously threatened by forest fires, which can result in extensive damage and financial losses. Strategies for managing and preventing wildfires depend heavily on the accurate prediction of burned areas. To create a predictive model for the burned areas caused by forest fires in Portugal's northeast, this study uses a machine learning technique, namely Neural Network. The model integrates meteorological and spatial-temporal data and makes use of regression algorithms. To maximize the performance of the model, data preprocessing, exploratory data analysis, model building, feature selection, and assessment are done. The results show that burned area is greatly influenced by meteorological factors, including temperature, humidity, and wind speed. The created model shows a respectable level of prediction accuracy of 97.11%, offering insightful information for efficient fire management and early warning systems. The potential for reducing the destructive effects of wildfires is enormous when machine learning is incorporated into the prediction of forest fires.