Convolutional Neural Network-based Smart Disaster Management Framework for Real-time Detection and Management of Forest Fires

Introduction: Timely detection of catastrophic natural disasters, such as forest fires, is critical to minimizing losses and ensuring rapid response. Artificial intelligence is increasingly being recognized as a valuable tool in enhancing various stages of disaster management. Method: This paper presents the development of a smart framework utilizing machine learning techniques for real-time detection and monitoring of natural disasters, specifically forest fires. The proposed approach employs a 10-layer convolutional neural network (CNN) that classifies aerial images into Fire, Non-Fire, and Smoke categories with high precision and speed. In addition to this, a CNN-based feature extraction process is performed and integrated with various ML classifiers, including support vector machine, k-nearest neighbor, decision tree, random forest, and extra trees. Results: Extensive performance analysis reveals that the proposed 10-layer CNN model outperforms other classifiers, achieving an accuracy of 97.64% in the binary classification of fire vs. nonfire and 95.61% in the three-class classification of Fire, Non-Fire and Smoke classes. Furthermore, a comparative study with existing state-of-the-art methods demonstrates the proposed model's superior performance in both accuracy and computational complexity. Conclusion: These results demonstrate the potential of the proposed CNN-based framework to serve as a reliable and effective tool for real-time disaster management across various applications, providing valuable support to emergency response teams in mitigating the impact of natural disasters.