Citation

Rehan, Mahmoud and Hashim, Noramiza and Anuar, Khairil and Mohd Isa, Wan Noorshahida (2025) Classification of Natural Disaster Images Using Convolutional Neural Network Models. In: 2025 IEEE Region 10 Conference, TENCON 2025, 27 October 2025 - 30 October 2025, Kota Kinabalu, Malaysia.

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Abstract

This paper explores the use of Convolutional Neural Networks (CNNs) for disaster classification, focusing on the EfficientNet architecture to classify four major natural disasters: floods, earthquakes, cyclones, and wildfires. EfficientNet stands out due to its novel compound scaling approach, which significantly enhances feature extraction from diverse image data while maintaining high computational efficiency. The model was trained using transfer learning on a carefully balanced dataset sourced from multiple disaster imagery repositories. Its performance was evaluated through accuracy, precision, recall, F1-score, and confusion matrices, ensuring a rigorous and reliable assessment of classification effectiveness. Experimental results demonstrate that EfficientNet consistently outperforms six competing models—VGG16, ResNet50, MobileNet, ARCNet-MobileNet, RescueNet, and ARCNet-VGG16. Notably, EfficientNet achieved the highest accuracy of 94% while requiring the shortest training time of only 13.5 minutes for 10 epochs. These findings highlight EfficientNet’s scalability, robustness, and reliability, making it an excellent candidate for deployment in realworld, image-based disaster management and early warning systems.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Natural disaster classification, efficientNet, deep learning, aerial images
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	20 Apr 2026 04:21
Last Modified:	20 Apr 2026 04:21
URII:	http://shdl.mmu.edu.my/id/eprint/15786

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