Evaluation of Flood Inundation Image Detection Performance Using Deep Learning
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Floods are the most frequently occurring natural disasters, significantly impacting the environment and society. As part of natural disaster mitigation, the impacts could be reduced through predictive techniques using deep learning for semantic segmentation of inundation images. Therefore, this research aims to evaluate the performance of deep learning architectures in segmenting inundation images using the Flood Segmentation dataset, which comprised 290 aerial images. The following segmentation architectures, U-Net, SegNet, and LinkNet, were compared using backbones such as MobileNet, ResNet, EfficientNet, and VGG, as well as optimizers including Adam, SGD, AdaDelta, and RMSProp. Performance was assessed using Intersection over Union (IoU) score, precision, F1-score, recall, and accuracy metrics. The results showed that U-Net achieved the highest performance with IoU, precision, F1-score, recall, and accuracy of 0.767, 0.862, 0.866, 0.876, and 0.899, respectively. Regarding the backbones, MobileNet excelled with IoU, precision, F1-score, recall, and accuracy of 0.764, 0.866, 0.865, 0.869, and 0.898, respectively. The Adam optimizer outperformed others, yielding IoU, precision, F1-score, recall, and accuracy of 0.712, 0.807, 0.824, 0.873, and 0.843. In conclusion, the combination of U-Net with MobileNet backbone and Adam optimizer was the most effective architecture for flood inundation image segmentation, offering a robust foundation for prediction systems.
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[1] Nkwunonwo, U. C., Whitworth, M., & Baily, B. (2020). A review of the current status of flood modelling for urban flood risk management in the developing countries. Scientific African, 7. doi:10.1016/j.sciaf.2020.e00269.
[2] Qin, Y. (2020). Urban flooding mitigation techniques: A systematic review and future studies. Water (Switzerland), 12(12), 3579. doi:10.3390/w12123579.
[3] Kourtis, I. M., Tsihrintzis, V. A., & Baltas, E. (2020). A robust approach for comparing conventional and sustainable flood mitigation measures in urban basins. Journal of Environmental Management, 269. doi:10.1016/j.jenvman.2020.110822.
[4] Gessang, O. M., & Lasminto, U. (2020). The flood prediction model using Artificial Neural Network (ANN) and weather Application Programming Interface (API) as an alternative effort to flood mitigation in the Jenelata Sub-watershed. IOP Conference Series: Materials Science and Engineering, 930(1), 12080. doi:10.1088/1757-899X/930/1/012080.
[5] Nemni, E., Bullock, J., Belabbes, S., & Bromley, L. (2020). Fully convolutional neural network for rapid flood segmentation in synthetic aperture radar imagery. Remote Sensing, 12(16), 2532. doi:10.3390/RS12162532.
[6] Liu, X., Song, L., Liu, S., & Zhang, Y. (2021). A review of deep-learning-based medical image segmentation methods. Sustainability (Switzerland), 13(3), 1–29. doi:10.3390/su13031224.
[7] Munawar, H. S., Hammad, A. W. A., & Waller, S. T. (2021). A review on flood management technologies related to image processing and machine learning. Automation in Construction, 132. doi:10.1016/j.autcon.2021.103916.
[8] Mo, Y., Wu, Y., Yang, X., Liu, F., & Liao, Y. (2022). Review the state-of-the-art technologies of semantic segmentation based on deep learning. Neurocomputing, 493, 626–646. doi:10.1016/j.neucom.2022.01.005.
[9] Hao, S., Zhou, Y., & Guo, Y. (2020). A Brief Survey on Semantic Segmentation with Deep Learning. Neurocomputing, 406, 302–321. doi:10.1016/j.neucom.2019.11.118.
[10] Asgari Taghanaki, S., Abhishek, K., Cohen, J. P., Cohen-Adad, J., & Hamarneh, G. (2021). Deep semantic segmentation of natural and medical images: a review. Artificial Intelligence Review, 54(1), 137–178. doi:10.1007/s10462-020-09854-1.
[11] Kar, M. K., Nath, M. K., & Neog, D. R. (2021). A Review on Progress in Semantic Image Segmentation and Its Application to Medical Images. SN Computer Science, 2(5), 397. doi:10.1007/s42979-021-00784-5.
[12] Muhadi, N. A., Abdullah, A. F., Bejo, S. K., Mahadi, M. R., & Mijic, A. (2021). Deep learning semantic segmentation for water level estimation using surveillance camera. Applied Sciences (Switzerland), 11(20), 9691. doi:10.3390/app11209691.
[13] Banan, A., Nasiri, A., & Taheri-Garavand, A. (2020). Deep learning-based appearance features extraction for automated carp species identification. Aquacultural Engineering, 89. doi:10.1016/j.aquaeng.2020.102053.
[14] Chen, C., Qin, C., Qiu, H., Tarroni, G., Duan, J., Bai, W., & Rueckert, D. (2020). Deep Learning for Cardiac Image Segmentation: A Review. Frontiers in Cardiovascular Medicine, 7. doi:10.3389/fcvm.2020.00025.
[15] Saood, A., & Hatem, I. (2021). COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Medical Imaging, 21(1), 19. doi:10.1186/s12880-020-00529-5.
[16] Akyel, C., & Arıcı, N. (2022). LinkNet-B7: Noise Removal and Lesion Segmentation in Images of Skin Cancer. Mathematics, 10(5), 736. doi:10.3390/math10050736.
[17] Sivagami, S., Chitra, P., Kailash, G. S. R., & Muralidharan, S. R. (2020). UNet Architecture Based Dental Panoramic Image Segmentation. 2020 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2020, 187–191. doi:10.1109/WiSPNET48689.2020.9198370.
[18] Gao, Y., Wang, G., Wang, G., Li, T., Zhang, S., Li, S., Zhang, Y., & Zhang, T. (2022). Two-dimensional phase unwrapping method using a refined D-LinkNet-based unscented Kalman filter. Optics and Lasers in Engineering, 152. doi:10.1016/j.optlaseng.2022.106948.
[19] Li, X., Xu, F., Liu, F., Lyu, X., Tong, Y., Xu, Z., & Zhou, J. (2023). A Synergistical Attention Model for Semantic Segmentation of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 61. doi:10.1109/TGRS.2023.3243954.
[20] Quang, N. H., Nguyen, M. N., Hung, N. M., Lee, H., & Kim, G. (2025). AI-based flood mapping from high-resolution ASNARO-2 images: case study of a severe event in the Center of Vietnam. Natural Hazards, 121(15), 17647–17675. doi:10.1007/s11069-025-07485-9.
[21] Lv, S., Meng, L., Edwing, D., Xue, S., Geng, X., & Yan, X. H. (2022). High-Performance Segmentation for Flood Mapping of HISEA-1 SAR Remote Sensing Images. Remote Sensing, 14(21), 5504. doi:10.3390/rs14215504.
[22] Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021). Loss odyssey in medical image segmentation. Medical Image Analysis, 71. doi:10.1016/j.media.2021.102035.
[23] Bangaru, S. S., Wang, C., Zhou, X., & Hassan, M. (2022). Scanning electron microscopy (SEM) image segmentation for microstructure analysis of concrete using U-net convolutional neural network. Automation in Construction, 144, 104602. doi:10.1016/j.autcon.2022.104602.
[24] Karim, F., Sharma, K., & Barman, N. R., (2022). Flood Area Segmentation. Kaggle, Mountain View, United States. Available online: https://www.kaggle.com/datasets/faizalkarim/flood-area-segmentation (accessed on November 2025)
[25] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham, Switzerland. doi:10.1007/978-3-319-24574-4_28.
[26] Safavi, F., Chowdhury, T., & Rahnemoonfar, M. (2021). Comparative Study Between Real-Time and Non-Real-Time Segmentation Models on Flooding Events. 2021 IEEE International Conference on Big Data (Big Data), 4199–4207. doi:10.1109/bigdata52589.2021.9671314.
[27] Siddique, N., Paheding, S., Elkin, C. P., & Devabhaktuni, V. (2021). U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications. IEEE Access, 9, 82031–82057. doi:10.1109/ACCESS.2021.3086020.
[28] Han, L., Liang, H., Chen, H., Zhang, W., & Ge, Y. (2022). Convective Precipitation Nowcasting Using U-Net Model. IEEE Transactions on Geoscience and Remote Sensing, 60. doi:10.1109/TGRS.2021.3100847.
[29] Dong, Z., Wang, G., Amankwah, S. O. Y., Wei, X., Hu, Y., & Feng, A. (2021). Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks. International Journal of Applied Earth Observation and Geoinformation, 102. doi:10.1016/j.jag.2021.102400.
[30] Şener, A., Doğan, G., & Ergen, B. (2023). A novel convolutional neural network model with hybrid attentional atrous convolution module for detecting the areas affected by the flood. Earth Science Informatics, 17(1), 193–209. doi:10.1007/s12145-023-01155-9.
[31] Yan, Z., Su, Y., Sun, H., Yu, H., Ma, W., Chi, H., Cao, H., & Chang, Q. (2022). SegNet-based left ventricular MRI segmentation for the diagnosis of cardiac hypertrophy and myocardial infarction. Computer Methods and Programs in Biomedicine, 227. doi:10.1016/j.cmpb.2022.107197.
[32] Chaurasia, A., & Culurciello, E. (2017). LinkNet: Exploiting encoder representations for efficient semantic segmentation. 2017 IEEE Visual Communications and Image Processing (VCIP), 8305148. doi:10.1109/vcip.2017.8305148.
[33] Yokoya, N., Yamanoi, K., He, W., Baier, G., Adriano, B., Miura, H., & Oishi, S. (2022). Breaking Limits of Remote Sensing by Deep Learning from Simulated Data for Flood and Debris-Flow Mapping. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–15. doi:10.1109/tgrs.2020.3035469.
[34] Tuyen, D. N., Tuan, T. M., Son, L. H., Ngan, T. T., Giang, N. L., Thong, P. H., Van Hieu, V., Gerogiannis, V. C., Tzimos, D., & Kanavos, A. (2021). A novel approach combining particle swarm optimization and deep learning for flash flood detection from satellite images. Mathematics, 9(22). doi:10.3390/math9222846.
[35] Sahithya, G., Harshitha, K., Priya, B. V. H., & Sirisha, B. (2023, March). Comparison of Backbones for Semantic Segmentation of X-Ray Microtomography Images using Convolutional Deep Neural Architecture. 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), 15-17 March, 2023, New Delhi, India.
[36] Liu, Y., Zhang, Z., Liu, X., Wang, L., & Xia, X. (2021). Efficient image segmentation based on deep learning for mineral image classification. Advanced Powder Technology, 32(10), 3885–3903. doi:10.1016/j.apt.2021.08.038.
[37] Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv Preprint, arXiv:1704.04861. doi:10.48550/arXiv.1704.04861.
[38] Sugimoto, Y., & Aono, M. (2021). Semantic Segmentation based on Extended MobileNet with FPN. 2021 8th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA), 1–6. doi:10.1109/icaicta53211.2021.9640256.
[39] Khatri, U., & Kwon, G. R. (2024). Diagnosis of Alzheimer’s disease via optimized lightweight convolution-attention and structural MRI. Computers in Biology and Medicine, 171. doi:10.1016/j.compbiomed.2024.108116.
[40] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. doi:10.1109/cvpr.2016.90.
[41] Feng, T., Liu, J., Fang, X., Wang, J., & Zhou, L. (2020). A double-branch surface detection system for armatures in vibration motors with miniature volume based on resnet-101 and FPN. Sensors (Switzerland), 20(8), 2360. doi:10.3390/s20082360.
[42] Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning. PMLR, 6105-6114.
[43] Gao, F., Sa, J., Wang, Z., & Zhao, Z. (2021). Cassava Disease Detection Method Based on EfficientNet. ICSAI 2021 - 7th International Conference on Systems and Informatics, 1–6. doi:10.1109/ICSAI53574.2021.9664101.
[44] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv Preprint, arXiv:1409.1556. doi:10.48550/arXiv.1409.1556.
[45] Sultana, F., Sufian, A., & Dutta, P. (2020). Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey. Knowledge-Based Systems, 201–202. doi:10.1016/j.knosys.2020.106062.
[46] Tabrizchi, H., Parvizpour, S., & Razmara, J. (2023). An Improved VGG Model for Skin Cancer Detection. Neural Processing Letters, 55(4), 3715–3732. doi:10.1007/s11063-022-10927-1.
[47] Hassan, E., Shams, M. Y., Hikal, N. A., & Elmougy, S. (2023). The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study. Multimedia Tools and Applications, 82(11), 16591–16633. doi:10.1007/s11042-022-13820-0.
[48] Kingma, D. P. (2014). Adam: A method for stochastic optimization. arXiv Preprint, arXiv:1412.6980. doi:10.48550/arXiv.1412.6980.
[49] Zeiler, M. D. (2012). Adadelta: an adaptive learning rate method. arXiv Preprint, arXiv:1212.5701. doi:10.48550/arXiv.1212.5701.
[50] Wang, X., Hu, Z., Shi, S., Hou, M., Xu, L., & Zhang, X. (2023). A deep learning method for optimizing semantic segmentation accuracy of remote sensing images based on improved UNet. Scientific Reports, 13(1), 7600. doi:10.1038/s41598-023-34379-2.
[51] Wang, Z., Wang, E., & Zhu, Y. (2020). Image segmentation evaluation: a survey of methods. Artificial Intelligence Review, 53(8), 5637–5674. doi:10.1007/s10462-020-09830-9.
[52] Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295–316. doi:10.1016/j.neucom.2020.07.061.
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