An Improved Forest Fire Detection Model Using Audio Classification and Machine Learning

Sudden wildfires cause significant global ecological damage. While satellite imagery has advanced early fire detection and mitigation, image-based systems face limitations including high false alarm rates, visual obstructions, and substantial computational demands, especially in complex forest terrains. To address these challenges, this study proposes a novel forest fire detection model utilizing audio classification and machine learning. We developed an audio-based pipeline using real-world environmental sound recordings. Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network (CNN), enabling the capture of distinctive fire acoustic signatures (e.g., crackling, roaring) that are minimally impacted by visual or weather conditions. Internet of Things (IoT) sound sensors were crucial for generating complex environmental parameters to optimize feature extraction. The CNN model achieved high performance in stratified 5-fold cross-validation (92.4% ± 1.6 accuracy, 91.2% ± 1.8 F1-score) and on test data (94.93% accuracy, 93.04% F1-score), with 98.44% precision and 88.32% recall, demonstrating reliability across environmental conditions. These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods. The findings suggest that acoustic sensing integrated with machine learning offers a powerful, low-cost, and efficient solution for real-time forest fire monitoring in complex, dynamic environments.