Machine Learning Models for Forest Fire Detection

One of the most serious natural disasters endangering forest resources and areas is forest fires. Decreased casualties and enhanced firefighting depend on precise and timely forest fire detection. Due to their reliance on ground inspection and field-of-view limitations, conventional firefighting techniques are inadequate for monitoring vast areas. Unmanned aerial vehicles (UAVs) are very difficult to identify forest fires, isolate fire pixels from the backdrop using Red Green Blue (RGB), grayscale color models, and extract luminance and chrominance from the original image. Over the past several years, machine-learning methods have been used to analyze forest images for detecting fires like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Random Forests, and many more. The goal is to determine the model for this crucial task by examining performance metrics across various algorithms. This study compares models like CNN, U-Net, and conventional machine learning models based on their accuracy, precision, and F1 score. Through thorough testing and analysis, this study aims to fully assess each model's strengths and flaws, ultimately aiding to development of more dependable and efficient methods for detecting forest fires.