Performance Evaluation of Deep Neural Networks for Forest Fire Classification

Authors : Oğuzhan Kilim, Şerafettin Atmaca, Tuncay Yiğit, Hamit Armağan

Pages : 31-45

Doi:10.57120/yalvac.1799284

View : 111 | Download : 239

Publication Date : 2025-10-29

Article Type : Research Paper

Abstract :Forest fires are destructive natural disasters that not only destroy vast forested areas but also threaten biodiversity, degrade air quality, damage agricultural land, and accelerate climate change. Due to rising global temperatures, prolonged droughts, and human-induced factors, the frequency and intensity of forest fires are increasing year by year. Consequently, the early detection and rapid classification of forest fires are critical for preventing loss of life and property and ensuring the effective management of disaster response processes. This study aims to present a deep learning-based approach for the early detection and classification of forest fires. In this context, four advanced convolutional neural network (CNN) architectures (Xception, InceptionV3, DenseNet121, and EfficientNetV2), which have shown outstanding success in image classification tasks in recent years, were comparatively evaluated for the classification of forest fire images. Training and testing procedures were performed using the Forest Fire Images dataset, consisting of fire and non-fire classes. The experimental results revealed that all models performed well in forest fire classification; however, the Xception model demonstrated superior performance, exhibiting higher accuracy than the others. These results emphasize that deep learning architectures are effective tools for the rapid and accurate classification of forest fires, thereby making significant contributions to forest fire monitoring and management strategies.
Keywords : Derin Öğrenme, Orman Yangını, Evrişimli Sinir Ağı

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