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

Abstract

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. non-fire 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.

© 2024 Bentham Science Publishers
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2024-12-13
2025-04-09

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/content/journals/swcc/10.2174/0122103279339983241028073645
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Convolutional Neural Network-based Smart Disaster Management Framework for Real-time Detection and Management of Forest Fires
Bentham Science Publishers ; https://doi.org/10.2174/0122103279339983241028073645
/content/journals/swcc/10.2174/0122103279339983241028073645
/content/journals/swcc/10.2174/0122103279339983241028073645
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  • Article Type:     Research Article
Keywords: convolutional neural network ; machine learning ; Artificial intelligence ; forest fire ; disaster management

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