A Critical Review of Flood Risk Modelling and Mapping Using Deep Learning for Enhancing Climate Adaptation and Community Resilience

Abdel-Mooty, M. N., El-Dakhakhni, W., & Coulibaly, P. (2022). Data-Driven Community Flood Resilience Prediction. Water 2022, 14, 2120. Abdel-Mooty, M. N., Yosri, A., El-Dakhakhni, W., & Coulibaly, P. (2021). Community flood resilience categorization framework. International Journal of Disaster Risk Reduction, 61, 102349. Albadr, M. A., Tiun, S., Ayob, M., & Al-Dhief, F. (2020). Genetic algorithm based on natural selection theory for optimization problems. Symmetry, 12(11), 1758. Bala B. and Behal S.(2024), 'A Brief Survey of Data Preprocessing in Machine Learning and Deep Learning Techniques,' 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 2024, pp. 1755- 1762, doi: 10.1109/I-SMAC61858.2024.10714767. Ben-Enukora C.A, Okorie N, Ejem A.A, and Eze I,(2025) Flood risk communication, perception and precautionary behaviours among residents in flood-prone state in North-central, Nigeria,International Journal of Disaster Risk Reduction, Volume 128, 2025, 105719, ISSN 2212-4209, https://doi.org/10.1016/j.ijdrr.2025.105719. (https://www.sciencedirect.com/science/article/pii/S2212420925005436) Bilal M and Akhtar Z (2024) Enhancing Weather Prediction Accuracy Using Hybrid Machine Learning Techniques: A Comprehensive Approach June 2024 Computers and Education Letters1(01):42-51 DOI: 10.64038/cel.0120245 License: CC BY 4.0 Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert systems with applications, 39(3), 3446- 3453. Cabrera, J. S., & Lee, H. S. (2019). Flood-prone area assessment using GIS-based multi- criteria analysis: A case study in Davao Oriental, Philippines. Water, 11(11), 2203. Celik, I., Gedrimiene, E., Siklander, S., & Muukkonen, H. (2024). The affordances of artificial intelligence-based tools for supporting 21st-century skills: A systematic review of empirical research in higher education. Australasian Journal of Educational Technology, 40(3), 19-38. Chabot, M., & Bertrand, J. L. (2025). Adaptive flood risk management: A decision support system integrating deep learning, digital twins, and economic risk assessment. Global Environmental Change, 95, 103069. Chen, J., Huang, G., & Chen, W. (2021). Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models. Journal of environmental management, 293, 112810. Corbane, C., Syrris, V., Sabo, F.(2021). Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery. Neural Comput & Applic 33, 6697–6720 (2021). https://doi.org/10.1007/s00521-020-05449-7 Deng, Y., & Lu, X. (2026). Explainable Machine Learning-Based Urban Waterlogging Prediction Framework. Urban Science, 10(3), 156. https://doi.org/10.3390/urbansci10030156 Diriba, D., Takele, T., Karuppannan, S., & Husein, M. (2024). Flood hazard analysis and risk assessment using remote sensing, GIS, and AHP techniques: a case study of the Gidabo Watershed, main Ethiopian Rift, Ethiopia. Geomatics, Natural Hazards and Risk, 15(1), 2361813. Eini, M., Kaboli, H. S., Rashidian, M., & Hedayat, H. (2020). Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts. International Journal of Disaster Risk Reduction, 50, 101687. EM-DAT, 2015). IJEMT Forootan, E., Kosary, M., Farzaneh, S., Kodikara, T., Vielberg, K., Fernandez-Gomez, I., ... & Schumacher, M. (2022). Forecasting global and multi-level thermospheric neutral density and ionospheric electron content by tuning models against satellite-based accelerometer measurements. Scientific Reports, 12(1), 2095. Forster, P. M. F., & Taylor, K. E. (2006). Climate forcings and climate sensitivities diagnosed from coupled climate model integrations. Journal of climate, 19(23), 6181- 6194. Ghaith, M., Yosri, A., & El-Dakhakhni, W. (2022). Synchronization-enhanced deep learning early flood risk predictions: The core of data-driven city digital twins for climate resilience planning. Water, 14(22), 3619. Gyang, P. A., Akomolafe, O., Lotsah, M., & Opoku, L. K. (2025). Climate change and flood risk in the US: A mini review of models future scenarios and adaptation strategies. World Journal of Advanced Engineering Technology and Sciences, 14(1), 185-190. Hallegatte, S., Green, C., Nicholls, R. J., & Corfee-Morlot, J. (2013). Future flood losses in major coastal cities. Nature climate change, 3(9), 802-806. Haque, C. E., Azad, M. A. K., & Choudhury, M. U. I. (2022). Social learning, innovative adaptation and community resilience to disasters: the case of flash floods in Bangladesh. Disaster Prevention and Management: An International Journal, 31(5), 601-618. Igbodiegwu G C, Leibrecht M, Ugochukwu O.M, Ebuka B.O Yang Z, (2026) Croaker aquaculture to offset declining fisheries in Lagos State, Nigeria, Ocean & Coastal Management, 10.1016/j.ocecoaman.2026.108109, 275, (108109), (2026). Jeffers J.M (2014) Environmental knowledge and human experience: Using a historical analysis of flooding in Ireland to challenge contemporary risk narratives and develop creative policy alternatives March 2014 Environmental Hazards13(3) DOI: 10.1080/17477891.2014.902800 Jolliff, J. K., Kindle, J. C., Shulman, I., Penta, B., Friedrichs, M. A., Helber, R., & Arnone, R. A. (2009). Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment. Journal of Marine Systems, 76(1-2), 64-82. Karthiyayini, J., Jain, A., Prasad, K. S., Abedi, T. A. A. U., Prasanna, Y., & Murugesan, K. (2025, February). Flood Prediction and Adaptive Farming Solutions Using IoT, Machine Learning, and Remote Sensing for Climate-Resilient Agriculture. In 2025 International Conference on Intelligent Control, Computing and Communications (IC3) (pp. 777-782). IEEE. Khatana, K., Soni, V., Akbar, A., Bharti, D., Srivastava, V., Saxena, R., & Ansari, S. (2025). Adaptation to Achieve Climate Resilience Through Advanced Techniques like Remote Sensing and Machine Learning. In Climate Resilience: Impact of Quantum Computing and Artificial Intelligence on Urban Planning (pp. 293-311). Cham: Springer Nature Switzerland. Lei, L., Chen, W., Wu, B., Chen, C., & Liu, W. (2021). A building energy consumption prediction model based on rough set theory and deep learning algorithms. Energy and Buildings, 240, 110886. Liu, Z., Coleman, N., Patrascu, F. I., Yin, K., Li, X., & Mostafavi, A. (2025). Artificial intelligence for flood risk management: A comprehensive state-of-the-art review and future directions. International Journal of Disaster Risk Reduction, 117, 105110. Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166-177. IJEMT Mabon, L. (2020). Making climate information services accessible to communities: What can we learn from environmental risk communication research?. Urban climate, 31, 100537. Mashwama, N. X., & Phesa, M. (2025). Systematic Review of Multidimensional Assessment of Coastal Infrastructure Resilience to Climate-Induced Flooding: Integrating Structural Vulnerability, System Capacity, and Organizational Preparedness. Climate, 13(9), 192. https://doi.org/10.3390/cli13090192 Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International journal of remote sensing, 39(9), 2784-2817. Meraihi, Y., Gabis, A. B., Ramdane-Cherif, A., & Acheli, D. (2021). A comprehensive survey of Crow Search Algorithm and its applications. Artificial Intelligence Review, 54(4). Mohamed M. MorsyJonathan L. lO'Neil G.L Huxley C (2018) A Cloud-Based Flood Warning System for Forecasting Impacts to Transportation Infrastructure Systems May 2018 Environmental Modelling & Software107 DOI: 10.1016/j.envsoft.2018.05.007 Oyekale, T. O., & Oyekale, A. S. (2013). Assessment of multidimensional poverty in rural and urban Nigeria: evidence from demographic and health survey . Journal of Human Ecology, 42(2), 141-154. Patel, A., Bilinska, J., Tam, J. C., Fontoura, D. D. S., Mason, C. Y., Daunt, A., ... & Nori, A. (2022). Clinical features and novel presentations of human monkeypox in a central London centre during the 2022 outbreak: descriptive case series. bmj, 378. Pour, M. A., Ghiasi, M. B., & Karkehabadi, A. (2025, January). Applying Machine Learning Tools for Urban Resilience Against Floods. In 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (pp. 1-6). IEEE. Qin, X., Wang, S., Meng, M., Long, H., Zhang, H., & Shi, H. (2025). Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability. npj Urban Sustainability, 5(1), 19. Razavi-Termeh, S. V., Hatamiafkoueieh, J., Sadeghi-Niaraki, A., Choi, S. M., & Al-Kindi, K. M. (2025). A GIS-based multi-objective evolutionary algorithm for landslide susceptibility mapping. Stochastic Environmental Research and Risk Assessment, 39(10), 4269-4294. Rentschler J, Salhab M, Jafino BA. (2022)Flood exposure and poverty in 188 countries. Nat Commun. 2022 Jun 28;13(1):3527. doi: 10.1038/s41467-022-30727-4. PMID: 35764617; PMCID: PMC9240081. Ringo J, Sabai S, and Mahenge A (2025) Contribution of flood early warning response measures in reducing flood effects in Kilosa District, Tanzania,Climate Services, Volume 37, 2025, 100534,ISSN 2405-8807, https://doi.org/10.1016/j.cliser.2024.100534. (https://www.sciencedirect.com/science/article/pii/S240588072400089X) Saikat, M. H. (2025). AI-Powered Flood Risk Prediction and Mapping for Urban Resilience. Authorea Preprints. Salami RO, von Meding JK, Giggins H. Vulnerability of human settlements to flood risk in the core area of Ibadan metropolis, Nigeria. Jamba. 2017 Nov 24;9(1):371. doi: 10.4102/jamba.v9i1.371. PMID: 29955336; PMCID: PMC6014243. Shabanpour, N., Razavi-Termeh, S. V., Sadeghi-Niaraki, A., Choi, S. M., & Abuhmed, T. IJEMT (2022). Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping. International Journal of Applied Earth Observation and Geoinformation, 112, 102854. Shetty, S. S., Deepthi, D., Harshitha, S., Sonkusare, S., Naik, P. B., & Madhyastha, H. (2023). Environmental pollutants and their effects on human health. Heliyon, 9(9). Taylor, K. E. (2001), Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106(D7), 7183– 7192, http://dx.doi.org/10.1029/2000JD900719. Thapa, P., Thapa, N., & Thapa, M. (2025). Deep Learning-Enhanced Flood Risk Mapping for Urban Planning and Disaster Mitigation in the Morang District, Nepal Himalaya. In The Himalaya Dilemma: Navigating Risk, Vulnerability, and Resilience in Geohazard-Prone Regions (pp. 609-625). Cham: Springer Nature Switzerland. Van Pham, T., Bui, D. X., Do, T. A. T., & Do, A. N. T. (2025). Assessing flood susceptibility in Hanoi using machine learning and remote sensing: implications for urban health and resilience. Natural Hazards, 1-22. Wang, Y., Zhang, P., Xie, Y., Chen, L., & Cai, Y. (2024). Machine learning insights into the evolution of flood Resilience: a synthesized framework study. Journal of Hydrology, 643, 131991. Wang, Y., Zhang, P., Xie, Y., Chen, L., & Li, Y. (2025). Toward explainable flood risk prediction: Integrating a novel hybrid machine learning model. Sustainable Cities and Society, 120, 106140. Week, D. A., and C. H. Wizor. 2020. “Effects of Flood on Food Security, Livelihood and Socio-Economic Characteristics in the Flood-Prone Areas of the Core Niger Delta, Nigeria”. Asian Journal of Geographical Research 3 (1):1-17. https://doi.org/10.9734/ajgr/2020/v3i130096. Yosri, A., Ghaith, M., & El-Dakhakhni, W. (2024). Deep learning rapid flood risk predictions for climate resilience planning. Journal of Hydrology, 631, 130817. Zhang, F., Yue, J., Zhou, C., Shi, X., Wu, B., & Ao, T. (2025). Long Short-Term Memory Based Runoff Simulation and Short-Term Forecasting for Alpine Regions: A Case Study in the Upper Jinsha River Basin. Water, 17(21), 3117. https://doi.org/10.3390/w17213117 Zhang, W., Hu, B., Liu, Y., Zhang, X., & Li, Z. (2023). Urban flood risk assessment through the integration of natural and human resilience based on machine learning models. Remote Sensing, 15(14), 3678.