Enhanced Forest Fire Detection Using a Combined SVM and Deep CNN Approach

Ridha Ilyas, Bendjillali; Mohammed Sofiane, Bendelhoum; Ali Abderrazak, Tadjeddine; Miloud, Kamline

Algerian Journal of Renewable Energy and Sustainable Development
Volume 6, Numéro 2, Pages 144-152
2024-12-15

Enhanced Forest Fire Detection Using A Combined Svm And Deep Cnn Approach

Authors : Ridha Ilyas Bendjillali . Mohammed Sofiane Bendelhoum . Ali Abderrazak Tadjeddine . Miloud Kamline .

Abstract

Our proposed approach for forest fire detection presents a significant advancement over existing techniques. By integrating SVM-based classification with state-of-the-art deep CNN architectures specifically PNasNet-5 and NFNet-F5, we achieve outstanding accuracy. Notably, our method attains a remarkable 100% training set accuracy on NFNet-F5. This exceptional accuracy enhances early fire prediction and minimizes false alarm rates, contributing significantly to environmental conservation and human life preservation. Moreover, our adept utilization of fine-tuning techniques effectively addresses challenges related to poor generalization and overfitting, thereby further enhancing the overall efficacy of our innovative approach.