An Intelligent Flood Monitoring Framework Using Deep Convolutional Neural Networks
Keywords:
Deep convolutional neural networks, Deep learning, Disaster management, Environmental surveillance, Flood monitoring, Image classification, Intelligent alert system, Real-time detection, Remote sensing, Smart infrastructureAbstract
Floods pose a significant threat to lives, infrastructure, and economies worldwide, necessitating timely and accurate monitoring systems. Traditional flood detection methods often suffer from delays, limited coverage, and reliance on manual observation. This research presents an intelligent flood monitoring framework powered by Deep Convolutional Neural Networks (CNNs), designed to automatically detect and classify flood events from real-time visual data. The proposed system utilizes a custom-trained CNN model on diverse flood and non-flood imagery, enabling high-accuracy classification in dynamic environmental conditions. The framework integrates seamlessly with alert mechanisms to generate real-time notifications for rapid response. Experimental evaluations demonstrate the model’s robustness, achieving high precision and recall across varied terrain and water levels. This work contributes to the development of an effective, scalable, and intelligent flood surveillance system suitable for deployment in disaster-prone regions and integration with smart city infrastructure.
References
B. Zhao, H. Sui, C. Xu, and J. Liu, “Deep Learning Approach for Flood Detection Using Sar Image: A Case Study in Xinxiang,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, B32022, pp. 1197–1202, May 2022, doi: https://doi.org/10.5194/isprs-archives-xliii-b3-2022-1197-2022.
J. W. Kirchner, "Getting the Right Answers for the Right Reasons: Linking Measurements, Analyses, and Models to Advance the Science of Hydrology," Water Resources Research, vol. 42, no. 3, p. W03S04, Mar. 2006. https://doi.org/10.1029/2005WR004362
B. Pradhan and S. Lee, "Utilization of Optical Remote Sensing Data and GIS Tools for Regional Landslide Hazard Analysis Using an Artificial Neural Network Model," Earth Science Frontiers, vol. 14, no. 6, pp. 143–152, Dec. 2007. https://doi.org/10.1016/S1872-5791(08)60008-1
J. Chen, M. Zhou, D. Zhang, H. Huang, and F. Zhang, “Quantification of water inflow in rock tunnel faces via convolutional neural network approach,” Automation in Construction, vol. 123, p. 103526, Mar. 2021, doi: https://doi.org/10.1016/j.autcon.2020.103526.
B. Rouet-Leduc, C. Hulbert, N. Lubbers, K. Barros, C. J. Humphreys, and P. A. Johnson, “Machine Learning Predicts Laboratory Earthquakes,” Geophysical Research Letters, vol. 44, no. 18, pp. 9276–9282, Sep. 2017, doi: https://doi.org/10.1002/2017gl074677.
E. Nemni, J. Bullock, S. Belabbes, and L. Bromley, "Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery," Remote Sensing, vol. 12, no. 16, p. 2532, Aug. 2020. https://doi.org/10.3390/rs12162532
P. Akiva, M. Purri, K. Dana, B. Tellman, and T. Anderson, "H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement," Arxiv Preprint arXiv:2010.05309, Oct. 2020. https://doi.org/10.1109/WACV48630.2021.00016
A. Mosavi, P. Ozturk, and K. Chau, “Flood Prediction Using Machine Learning Models: Literature Review,” Water, vol. 10, no. 11, p. 1536, Oct. 2018, doi: https://doi.org/10.3390/w10111536.
Z. Situ, Q. Wang, S. Teng, W. Feng, "Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal Feature Fusion," Arxiv Preprint arXiv:2304.09994, Apr. 2023. https://doi.org/10.1016/j.jhydrol.2024.130743
R. Yadav, A. Nascetti, and Y. Ban, "Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data," Arxiv Preprint arXiv:2204.09387, Apr. 2022. https://doi.org/10.1109/IGARSS46834.2022.9883132
Y. Chen, Y. Kang, Y. Chen, and Z. Wang, "Probabilistic Forecasting with Temporal Convolutional Neural Network," Arxiv Preprint arXiv:1906.01149, Jun. 2019. https://doi.org/10.1016/j.neucom.2020.03.011
B. Zhao et al., "Convolutional Neural Networks for Time Series Classification," Journal of Systems Engineering and Electronics, vol. 28, no. 1, pp. 162–169, Feb. 2017. https://doi.org/10.21629/JSEE.2017.01.18
G. Petneházi, "QCNN: Quantile Convolutional Neural Network," Arxiv Preprint arXiv:1908.10396, Aug. 2019. https://www.researchgate.net/publication/335318880_QCNN_Quantile_Convolutional_Neural_Network
S. Chauhan, M. Sharma, M. K. Arora, and N. K. Gupta, “Landslide Susceptibility Zonation through ratings derived from Artificial Neural Network,” International Journal of Applied Earth Observation and Geoinformation, vol. 12, no. 5, pp. 340–350, Oct. 2010, doi: https://doi.org/10.1016/j.jag.2010.04.006.
T. R. Petty and P. Dhingra, "Streamflow Hydrology Estimate Using Machine Learning (SHEM)," JAWRA Journal of the American Water Resources Association, vol. 53, no. 4, pp. 942–953, Aug. 2017. https://doi.org/10.1111/1752-1688.12555
D. M. Tartakovsky, "Delineation of Geologic Facies with Statistical Learning Theory," Geophysical Research Letters, vol. 31, no. 15, p. L15501, Aug. 2004. https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2004GL020864
D. Kawabata and J. Bandibas, "Landslide Susceptibility Mapping Using Geological Data, a DEM from ASTER Images and an Artificial Neural Network (ANN)," Geomorphology, vol. 113, no. 1–2, pp. 97–109, Dec. 2009. https://doi.org/10.1016/j.geomorph.2009.06.006
P. R. Kumbam and K. M. Vejre, "FloodLense: A Framework for ChatGPT-based Real-time Flood Detection," Arxiv Preprint arXiv:2401.15501, Jan. 2024. https://doi.org/10.48550/arXiv.2401.15501
A. Thessen, "Adoption of Machine Learning Techniques in Ecology and Earth Science," One Ecosystem, vol. 1, p. e8621, Jun. 2016. https://oneecosystem.pensoft.net/article/8621/
A. Karpatne, I. Ebert-Uphoff, S. Ravela, H. A. Babaie, and V. Kumar, “Machine Learning for the Geosciences: Challenges and Opportunities,” IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 8, pp. 1544–1554, Aug. 2019, doi: https://doi.org/10.1109/TKDE.2018.2861006.
A. Borovykh, S. Bohte, and C. W. Oosterlee, "Conditional Time Series Forecasting with Convolutional Neural Networks," Arxiv Preprint arXiv:1703.04691, Mar. 2017. https://doi.org/10.48550/arXiv.1703.04691
R. Mittelman, "Time-series Modeling with Undecimated Fully Convolutional Neural Networks," Arxiv Preprint arXiv:1508.00317, Aug. 2015. https://doi.org/10.48550/arXiv.1508.00317
G. De'ath and K. E. Fabricius, "Classification and Regression Trees: A Powerful Yet Simple Technique for Ecological Data Analysis," Ecology, vol. 81, no. 11, pp. 3178–3192, Nov. 2000. https://doi.org/10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2
K. Zhao, S. Popescu, X. Meng, Y. Pang, and M. Agca, “Characterizing Forest canopy structure with lidar composite metrics and machine learning,” Remote Sensing of Environment, vol. 115, no. 8, pp. 1978–1996, Aug. 2011, doi: https://doi.org/10.1016/j.rse.2011.04.001.
J. J. Lawler, D. White, R. P. Neilson, And A. R. Blaustein, “Predicting climate-induced range shifts: model differences and model reliability,” Global Change Biology, vol. 12, no. 8, pp. 1568–1584, Aug. 2006, doi: https://doi.org/10.1111/j.1365-2486.2006.01191.x.
J. Dou, "An Integrated Artificial Neural Network Model for the Landslide Susceptibility Assessment of Osado Island, Japan," Natural Hazards, vol. 78, no. 1, pp. 1–20, Jan. 2015. https://link.springer.com/article/10.1007/s11069-015-1799-2
M. Marjanović, "Landslide Susceptibility Assessment Using SVM Machine Learning Algorithm," Engineering Geology, vol. 123, no. 3, pp. 225–234, Nov. 2011. https://doi.org/10.1016/j.enggeo.2011.09.006
