{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T19:48:00Z","timestamp":1791748080712,"version":"4.3.4"},"reference-count":157,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T00:00:00Z","timestamp":1790985600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Water Research"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.watres.2026.127064","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T15:21:03Z","timestamp":1790954463000},"page":"127064","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["Enhancing urban flood risk assessment: A PCA-integrated deep learning surrogate for hazard and damage prediction"],"prefix":"10.1016","volume":"308","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3195-8751","authenticated-orcid":false,"given":"Hyeontae","family":"Moon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyung-Tak","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gilho","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.watres.2026.127064_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.scs.2025.106277","article-title":"A surrogate machine learning modeling approach for enhancing the efficiency of urban flood modeling at metropolitan scales","volume":"123","author":"Aderyani","year":"2025","journal-title":"Sustain. Cities Soc"},{"key":"10.1016\/j.watres.2026.127064_bib0002","article-title":"A review of recent advances in urban flood research","volume":"19","author":"Agonafir","year":"2023","journal-title":"Water Secur"},{"key":"10.1016\/j.watres.2026.127064_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2025.106691","article-title":"Transfer learning using the global Caravan dataset for developing a local river streamflow prediction model","volume":"194","author":"Alzhanov","year":"2025","journal-title":"Environ. Model. Softw"},{"key":"10.1016\/j.watres.2026.127064_bib0005","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11069-008-9277-8","article-title":"Flood risk analyses\u2013how detailed do we need to be\u2013","volume":"49","author":"Apel","year":"2009","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.watres.2026.127064_bib0007","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11069-005-8603-7","article-title":"A probabilistic modelling system for assessing flood risks","volume":"38","author":"Apel","year":"2006","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.watres.2026.127064_bib0008","doi-asserted-by":"crossref","DOI":"10.1111\/jfr3.70097","article-title":"The sensitivity of urban pluvial flooding to the temporal distribution of rainfall within design storms","volume":"18","author":"Asher","year":"2025","journal-title":"J. Flood Risk Manag."},{"key":"10.1016\/j.watres.2026.127064_bib0009","first-page":"1","article-title":"Leveraging coupled hydrodynamic with data-driven GeoAI models for advancing systemic compound flood risk evaluation in coastal urban areas","author":"Atmaja","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.watres.2026.127064_bib0010","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/s44304-025-00122-2","article-title":"A machine learning-based prediction-to-map framework for rapid and accurate spatial flood prediction","volume":"2","author":"Bao","year":"2025","journal-title":"npj Nat. Hazards"},{"key":"10.1016\/j.watres.2026.127064_bib0012","doi-asserted-by":"crossref","first-page":"5871","DOI":"10.5194\/hess-29-5871-2025","article-title":"Unveiling the limits of deep learning models in hydrological extrapolation tasks","volume":"29","author":"Baste","year":"2025","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0013","doi-asserted-by":"crossref","DOI":"10.1029\/2020WR028673","article-title":"Combined modeling of US fluvial, pluvial, and coastal flood hazard under current and future climates","volume":"57","author":"Bates","year":"2021","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.watres.2026.127064_bib0014","first-page":"1","article-title":"Deep learning methods for flood mapping: a review of existing applications and future research directions","volume":"2022","author":"Bentivoglio","year":"2022","journal-title":"Hydrol. Earth Syst. Sci. Discuss."},{"key":"10.1016\/j.watres.2026.127064_bib0016","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.watres.2026.127064_bib0017","article-title":"Data driven real-time prediction of urban floods with spatial and temporal distribution","volume":"22","author":"Berkhahn","year":"2024","journal-title":"J. Hydrol. X"},{"key":"10.1016\/j.watres.2026.127064_bib0018","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1007\/s11269-018-1959-8","article-title":"Development and comparison of two fast surrogate models for urban pluvial flood simulations","volume":"32","author":"Berm\u00fadez","year":"2018","journal-title":"Water Resour. Manag."},{"key":"10.1016\/j.watres.2026.127064_bib0019","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3390\/geosciences8020069","article-title":"Impact of the rainfall duration and temporal rainfall distribution defined using the Huff curves on the hydraulic flood modelling results","volume":"8","author":"Bezak","year":"2018","journal-title":"Geosciences"},{"key":"10.1016\/j.watres.2026.127064_bib0021","first-page":"65","article-title":"Cumulative storm rainfall distributions: comparison of Huff curves","volume":"42","author":"Bonta","year":"2003","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0022","doi-asserted-by":"crossref","first-page":"6983","DOI":"10.1029\/2018WR023378","article-title":"Inside or outside: quantifying extrapolation across river networks","volume":"54","author":"Booker","year":"2018","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.watres.2026.127064_bib0023","doi-asserted-by":"crossref","first-page":"235","DOI":"10.3390\/geosciences12060235","article-title":"Mapping gully erosion variability and susceptibility using remote sensing, multivariate statistical analysis, and machine learning in South Mato Grosso","volume":"12","author":"Bouramtane","year":"2022","journal-title":"Brazil. Geosciences"},{"key":"10.1016\/j.watres.2026.127064_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.advwatres.2020.103821","article-title":"A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment","volume":"147","author":"Carreau","year":"2021","journal-title":"Adv. Water Resour."},{"key":"10.1016\/j.watres.2026.127064_bib0026","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1080\/1573062X.2024.2446528","article-title":"Recent advances and future challenges in urban pluvial flood modelling","volume":"22","author":"Cea","year":"2025","journal-title":"Urban Water J"},{"key":"10.1016\/j.watres.2026.127064_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2022.128086","article-title":"Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis","volume":"612","author":"Chang","year":"2022","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0028","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"XGBoost: a scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.watres.2026.127064_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2025.106350","article-title":"Fine-tuning long short-term memory models for seamless transition in hydrological modelling: from pre-training to post-application","volume":"186","author":"Chen","year":"2025","journal-title":"Environ. Model. Softw."},{"key":"10.1016\/j.watres.2026.127064_bib0030","first-page":"1","article-title":"Multi-dimensional flood damage analysis (I): principle and procedure","volume":"39","author":"Choi","year":"2006","journal-title":"J. Korea Water Resour. Assoc."},{"key":"10.1016\/j.watres.2026.127064_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.132564","article-title":"Real-time short-range flood forecasting based on a watershed scale 2-D hydrodynamic model and high-resolution precipitation forecast ensemble","volume":"650","author":"Coelho","year":"2025","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0032","doi-asserted-by":"crossref","first-page":"3261","DOI":"10.5194\/nhess-20-3261-2020","article-title":"Forecasting flood hazards in real time: a surrogate model for hydrometeorological events in an Andean watershed","volume":"20","author":"Contreras","year":"2020","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0033","doi-asserted-by":"crossref","first-page":"489","DOI":"10.5194\/nhess-20-489-2020","article-title":"Measuring compound flood potential from river discharge and storm surge extremes at the global scale","volume":"20","author":"Couasnon","year":"2020","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0034","doi-asserted-by":"crossref","DOI":"10.1080\/17538947.2025.2515268","article-title":"A method for assessing urban flood impacts by combining hydrodynamic models with entity semantic evolution","volume":"18","author":"Dang","year":"2025","journal-title":"Int. J. Digit. Earth"},{"key":"10.1016\/j.watres.2026.127064_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.111031","article-title":"Application of machine learning-based surrogate models for urban flood depth modeling in Ho Chi Minh City, Vietnam","volume":"150","author":"Dang","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.watres.2026.127064_bib0036","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s11069-010-9675-6","article-title":"Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates","volume":"58","author":"de Moel","year":"2011","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.watres.2026.127064_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2025.106786","article-title":"A framework for the evaluation of flood inundation predictions over extensive benchmark databases","volume":"196","author":"Devi","year":"2026","journal-title":"Environ. Model. Softw."},{"key":"10.1016\/j.watres.2026.127064_bib0038","article-title":"Developing flood mapping procedure through optimized machine learning techniques. Case study: prahova river basin, Romania","volume":"54","author":"Diaconu","year":"2024","journal-title":"J. Hydrol. Reg. Stud."},{"key":"10.1016\/j.watres.2026.127064_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.168814","article-title":"Physics-informed neural networks as surrogate models of hydrodynamic simulators","volume":"912","author":"Donnelly","year":"2024","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.watres.2026.127064_bib0040","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.5194\/essd-14-1549-2022","article-title":"A new dataset of river flood hazard maps for Europe and the Mediterranean Basin","volume":"14","author":"Dottori","year":"2022","journal-title":"Earth Syst. Sci. Data"},{"key":"10.1016\/j.watres.2026.127064_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.104152","article-title":"Fast urban flood modeling informing response decisions: model development and future perspectives","volume":"70","author":"Duan","year":"2026","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.watres.2026.127064_bib0042","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-07088-1","article-title":"Convolutional neural networks with transfer learning for natural river flow prediction in ungauged basins","volume":"15","author":"Echternacht","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.watres.2026.127064_bib0043","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s00477-021-02060-7","article-title":"Tackling random fields non-linearities with unsupervised clustering of polynomial chaos expansion in latent space: application to global sensitivity analysis of river flooding","volume":"36","author":"El Garroussi","year":"2022","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"10.1016\/j.watres.2026.127064_bib0044","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2025.133632","article-title":"Integrating net rainfall calculation in deep learning-based surrogate modeling frameworks for 2D flood prediction","volume":"661","author":"Farf\u00e1n-Dur\u00e1n","year":"2025","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0045","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1007\/s11069-020-04480-0","article-title":"Urbanization impacts on flood risks based on urban growth data and coupled flood models","volume":"106","author":"Feng","year":"2021","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.watres.2026.127064_bib0046","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1038\/s43017-020-00128-6","article-title":"Anthropogenic intensification of short-duration rainfall extremes","volume":"2","author":"Fowler","year":"2021","journal-title":"Nat. Rev. Earth Environ."},{"key":"10.1016\/j.watres.2026.127064_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2024.121202","article-title":"Assessment of surrogate models for flood inundation: the physics-guided LSG model vs. state-of-the-art machine learning models","volume":"252","author":"Fraehr","year":"2024","journal-title":"Water Res"},{"key":"10.1016\/j.watres.2026.127064_bib0048","doi-asserted-by":"crossref","DOI":"10.1029\/2024GL109424","article-title":"Rapid inundation mapping using the US National Water Model, satellite observations, and a convolutional neural network","volume":"51","author":"Frame","year":"2024","journal-title":"Geophys. Res. Lett."},{"key":"10.1016\/j.watres.2026.127064_bib0049","doi-asserted-by":"crossref","first-page":"2979","DOI":"10.2166\/wst.2010.177","article-title":"Uncertainty in urban flood damage assessment due to urban drainage modelling and depth-damage curve estimation","volume":"61","author":"Freni","year":"2010","journal-title":"Water Sci. Technol."},{"key":"10.1016\/j.watres.2026.127064_bib0050","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0159791","article-title":"A review of flood loss models as basis for harmonization and benchmarking","volume":"11","author":"Gerl","year":"2016","journal-title":"PLOS ONE"},{"key":"10.1016\/j.watres.2026.127064_bib0052","doi-asserted-by":"crossref","first-page":"9925","DOI":"10.1007\/s11069-025-07186-3","article-title":"Exploring the impact of rainfall temporal distribution and critical durations on flood hazard modeling","volume":"121","author":"Gomes","year":"2025","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.watres.2026.127064_bib0053","series-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"10.1016\/j.watres.2026.127064_bib0054","doi-asserted-by":"crossref","DOI":"10.1111\/jfr3.12684","article-title":"Data-driven flood emulation: speeding up urban flood predictions by deep convolutional neural networks","volume":"14","author":"Guo","year":"2021","journal-title":"J. Flood Risk Manag."},{"key":"10.1016\/j.watres.2026.127064_bib0055","doi-asserted-by":"crossref","first-page":"2946","DOI":"10.3390\/w15162946","article-title":"Parameter optimization of coupled 1D\u20132D hydrodynamic model for urban flood inundation","volume":"15","author":"Ha","year":"2023","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.127064_bib0056","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1007\/s12205-015-0482-1","article-title":"Effect of threshold on the comparison of radar and rain gauge rain rate","volume":"19","author":"Ha","year":"2015","journal-title":"KSCE J. Civ. Eng."},{"key":"10.1016\/j.watres.2026.127064_bib0057","doi-asserted-by":"crossref","first-page":"3368","DOI":"10.3390\/w16233368","article-title":"Towards accurate flood predictions: a deep learning approach using Wupper River Data","volume":"16","author":"Hahn","year":"2024","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.127064_bib0058","doi-asserted-by":"crossref","DOI":"10.1016\/j.jemets.2025.100009","article-title":"Recent changes in summer rainfall characteristics in Korea","volume":"2","author":"Ho","year":"2025","journal-title":"J. Eur. Meteorol. Soc."},{"key":"10.1016\/j.watres.2026.127064_bib0059","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1007\/s41019-022-00193-5","article-title":"Dimensionality reduction in surrogate modeling: a review of combined methods","volume":"7","author":"Hou","year":"2022","journal-title":"Data Sci. Eng."},{"key":"10.1016\/j.watres.2026.127064_bib0060","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1029\/WR003i004p01007","article-title":"Time distribution of rainfall in heavy storms","volume":"3","author":"Huff","year":"1967","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.watres.2026.127064_bib0061","author":"Innovyze"},{"key":"10.1016\/j.watres.2026.127064_bib0062","series-title":"Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel On Climate Change","year":"2022"},{"key":"10.1016\/j.watres.2026.127064_bib0063","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2020.109854","article-title":"Non-intrusive reduced-order modeling using uncertainty-aware deep neural networks and proper orthogonal decomposition: application to flood modeling","volume":"424","author":"Jacquier","year":"2021","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.watres.2026.127064_bib0064","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2026.106978","article-title":"A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction","volume":"201","author":"Jahangir","year":"2026","journal-title":"Environ. Model. Softw."},{"key":"10.1016\/j.watres.2026.127064_bib0065","doi-asserted-by":"crossref","first-page":"3155","DOI":"10.5194\/nhess-24-3155-2024","article-title":"Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China","volume":"24","author":"Jam-Jalloh","year":"2024","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0066","article-title":"Principal component analysis: a review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0067","doi-asserted-by":"crossref","first-page":"3733","DOI":"10.5194\/nhess-12-3733-2012","article-title":"Comparative flood damage model assessment: towards a European approach","volume":"12","author":"Jongman","year":"2012","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0068","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.125481","article-title":"A deep convolutional neural network model for rapid prediction of fluvial flood inundation","volume":"590","author":"Kabir","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0069","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-33803-z","article-title":"Deep vision-based framework for coastal flood prediction under sea level rise and shoreline protection","volume":"16","author":"Karapetyan","year":"2026","journal-title":"Sci. Rep."},{"key":"10.1016\/j.watres.2026.127064_bib0070","doi-asserted-by":"crossref","first-page":"566","DOI":"10.3390\/w15030566","article-title":"A review of hydrodynamic and machine learning approaches for flood inundation modeling","volume":"15","author":"Karim","year":"2023","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.127064_bib0071","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2382577.2382579","article-title":"Leakage in data mining: formulation, detection, and avoidance","volume":"6","author":"Kaufman","year":"2012","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.watres.2026.127064_bib0072","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3390\/ijgi7010029","article-title":"Assessment of tangible direct flood damage using a spatial analysis approach under the effects of climate change: case study in an urban watershed in Hanoi, Vietnam","volume":"7","author":"Kefi","year":"2018","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"10.1016\/j.watres.2026.127064_bib0073","first-page":"749","article-title":"Analysis of building flood damage using HEC-RAS 2D and GIS","volume":"20","author":"Kim","year":"2024","journal-title":"J. Soc. Disaster Inf."},{"key":"10.1016\/j.watres.2026.127064_bib0074","first-page":"14","article-title":"Development of an analysis tool for the flood damage estimation model to support economic feasibility analysis in flood control","volume":"54","author":"Kim","year":"2021","journal-title":"Water Future"},{"key":"10.1016\/j.watres.2026.127064_bib0075","article-title":"Urban flood adaptation and optimization for net-zero: case study of Dongjak-gu, Seoul","volume":"41","author":"Kim","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"10.1016\/j.watres.2026.127064_bib0076","unstructured":"Kingma, D.P., Ba, J., 2014. Adam: a method for stochastic optimization. arXiv preprint."},{"key":"10.1016\/j.watres.2026.127064_bib0077","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.envsci.2014.10.013","article-title":"Combining hazard, exposure and social vulnerability to provide lessons for flood risk management","volume":"47","author":"Koks","year":"2015","journal-title":"Environ. Sci. Policy"},{"key":"10.1016\/j.watres.2026.127064_bib0078","doi-asserted-by":"crossref","first-page":"215","DOI":"10.3390\/hydrology11120215","article-title":"A systematic modular approach for the coupling of deep-learning-based models to forecast urban flooding maps in early warning systems","volume":"11","author":"Koltermann da Silva","year":"2024","journal-title":"Hydrology"},{"key":"10.1016\/j.watres.2026.127064_bib0080","doi-asserted-by":"crossref","first-page":"550","DOI":"10.3390\/w18050550","article-title":"Real-time forecasting and mapping flood extent from integrated hydrologic models and satellite remote sensing","volume":"18","author":"Krajewski","year":"2026","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.127064_bib0081","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1061\/(ASCE)HY.1943-7900.0000037","article-title":"Comparison of 1D\/1D and 1D\/2D coupled (sewer\/surface) hydraulic models for urban flood simulation","volume":"135","author":"Leandro","year":"2009","journal-title":"J. Hydraul. Eng."},{"key":"10.1016\/j.watres.2026.127064_bib0082","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.watres.2026.127064_bib0083","article-title":"Predicting peak inundation depths with a physics informed machine learning model","volume":"14","author":"Lee","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.watres.2026.127064_bib0084","doi-asserted-by":"crossref","first-page":"21459","DOI":"10.1007\/s11069-025-07639-9","article-title":"A residual correction approach for improving rainfall\u2013runoff model performance in flood early warning systems","volume":"121","author":"Lee","year":"2025","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.watres.2026.127064_bib0085","doi-asserted-by":"crossref","first-page":"971","DOI":"10.3390\/atmos11090971","article-title":"Integrated flood forecasting and warning system against flash rainfall in the small-scaled urban stream","volume":"11","author":"Lee","year":"2020","journal-title":"Atmosphere"},{"key":"10.1016\/j.watres.2026.127064_bib0086","doi-asserted-by":"crossref","first-page":"6735","DOI":"10.5194\/hess-29-6735-2025","article-title":"An illustrative introduction to the domain dependence of spatial principal component patterns","volume":"29","author":"Lehr","year":"2025","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0087","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.130871","article-title":"Flood forecasting based on radar precipitation nowcasting using U-net and its improved models","volume":"632","author":"Li","year":"2024","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0088","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2025.134763","article-title":"Surrogate modeling for rapid estimation of spatially-resolved flood damage: application to a coastal region","volume":"666","author":"Li","year":"2026","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0089","doi-asserted-by":"crossref","first-page":"3438","DOI":"10.3390\/w13233438","article-title":"Case study of urban flood inundation\u2013impact of temporal variability in rainfall events","volume":"13","author":"Li","year":"2021","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.127064_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.129945","article-title":"Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model","volume":"624","author":"Liao","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0091","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2021.126898","article-title":"U-FLOOD\u2013Topographic deep learning for predicting urban pluvial flood water depth","volume":"603","author":"L\u00f6we","year":"2021","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0092","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2022.105478","article-title":"Urban flood numerical simulation: research, methods and future perspectives","volume":"156","author":"Luo","year":"2022","journal-title":"Environ. Model. Softw."},{"key":"10.1016\/j.watres.2026.127064_bib0093","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.5194\/nhess-10-1697-2010","article-title":"Review article: assessment of economic flood damage","volume":"10","author":"Merz","year":"2010","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0094","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.jhydrol.2017.06.025","article-title":"Urbanisation impacts on storm runoff along a rural-urban gradient","volume":"552","author":"Miller","year":"2017","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0095","doi-asserted-by":"crossref","first-page":"8871","DOI":"10.1007\/s11069-024-06555-8","article-title":"Assessment of pluvial flood events based on monitoring and modeling of an old urban storm drainage in the city center of Yangon, Myanmar","volume":"120","author":"Min","year":"2024","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.watres.2026.127064_bib0097","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijdrr.2025.105743","article-title":"Deep learning-enhanced flood damage prediction: a DFNN-based hybrid approach with simplified inputs","volume":"128","author":"Mok","year":"2025","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"10.1016\/j.watres.2026.127064_bib0098","first-page":"1","article-title":"Emulating rainfall\u2013runoff-inundation model using deep neural network with dimensionality reduction","volume":"2","author":"Momoi","year":"2023","journal-title":"Artif. Intell. Earth Syst."},{"key":"10.1016\/j.watres.2026.127064_bib0099","doi-asserted-by":"crossref","first-page":"2606","DOI":"10.2166\/hydro.2023.203","article-title":"Urban flood forecasting using a hybrid modeling approach based on a deep learning technique","volume":"25","author":"Moon","year":"2023","journal-title":"J. Hydroinform."},{"key":"10.1016\/j.watres.2026.127064_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijdrr.2024.104542","article-title":"Mitigating urban flood hazards: hybrid strategy of structural measures","volume":"108","author":"Moon","year":"2024","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"10.1016\/j.watres.2026.127064_bib0101","doi-asserted-by":"crossref","DOI":"10.1111\/jfr3.13000","article-title":"Assessing future urban flood hazard: a comprehensive approach to estimating the implications of future rainfall scenarios","volume":"17","author":"Moon","year":"2024","journal-title":"J. Flood Risk Manag."},{"key":"10.1016\/j.watres.2026.127064_bib0102","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.13031\/trans.58.10715","article-title":"Hydrologic and water quality models: performance measures and evaluation criteria","volume":"58","author":"Moriasi","year":"2015","journal-title":"Trans. ASABE"},{"key":"10.1016\/j.watres.2026.127064_bib0103","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.3390\/w10111536","article-title":"Flood prediction using machine learning models: literature review","volume":"10","author":"Mosavi","year":"2018","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.127064_bib0104","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2023.105936","article-title":"Evaluation of surrogate flood models for the use in impact-based flood warning systems at national scale","volume":"173","author":"Mosimann","year":"2024","journal-title":"Environ. Model. Softw."},{"key":"10.1016\/j.watres.2026.127064_bib0105","series-title":"Proceedings of the 27th International Conference on Machine Learning (ICML-10)","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"Nair","year":"2010"},{"key":"10.1016\/j.watres.2026.127064_bib0106","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I: a discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0108","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2026.135410","article-title":"Intercomparison of flood inundation models across land use types and hydrological flood stages","volume":"673","author":"Nikrou","year":"2026","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0109","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2025.133063","article-title":"Dive into transfer-learning for daily rainfall\u2013runoff modeling in data-limited basins","volume":"657","author":"Ouyang","year":"2025","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0110","doi-asserted-by":"crossref","first-page":"4853","DOI":"10.1007\/s11269-025-04179-6","article-title":"The impact of temporal rainfall pattern uncertainties on water quantity and sediment transportation results of an integrated flood simulator","volume":"39","author":"Ouzounis","year":"2025","journal-title":"Water Resour. Manag."},{"key":"10.1016\/j.watres.2026.127064_bib0111","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.watres.2026.127064_bib0112","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.132334","article-title":"Rapid urban inundation prediction method based on numerical simulation and AI algorithm","volume":"647","author":"Pan","year":"2025","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0113","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2020.140011","article-title":"Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe","volume":"737","author":"Paprotny","year":"2020","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.watres.2026.127064_bib0114","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s11069-018-3462-1","article-title":"An integrated 1D\u20132D hydraulic modelling approach to assess the sensitivity of a coastal region to compound flooding hazard under climate change","volume":"98","author":"Pasquier","year":"2019","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.watres.2026.127064_bib0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijdrr.2023.104215","article-title":"Model parameter influence on probabilistic flood risk analysis","volume":"100","author":"Paulik","year":"2024","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"10.1016\/j.watres.2026.127064_bib0116","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.watres.2026.127064_bib0117","doi-asserted-by":"crossref","first-page":"15712","DOI":"10.1007\/s11356-015-4707-0","article-title":"Assessment of the service performance of drainage system and transformation of pipeline network based on urban combined sewer system model","volume":"22","author":"Peng","year":"2015","journal-title":"Environ. Sci. Pollut. Res."},{"key":"10.1016\/j.watres.2026.127064_bib0118","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2022.127476","article-title":"A critical review of real-time modelling of flood forecasting in urban drainage systems","volume":"607","author":"Piadeh","year":"2022","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0119","doi-asserted-by":"crossref","DOI":"10.1111\/jfr3.70184","article-title":"Approaching urban flood modelling according to available data: a state-of-the-art review and future challenges","volume":"19","author":"Pirone","year":"2026","journal-title":"J. Flood Risk Manag."},{"key":"10.1016\/j.watres.2026.127064_bib0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2021.126982","article-title":"Flood depth-damage and fragility functions derived with structured expert judgment","volume":"603","author":"Pita","year":"2021","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0121","series-title":"RiskScape: Flood Fragility Methodology","author":"Reese","year":"2010"},{"key":"10.1016\/j.watres.2026.127064_bib0122","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.125178","article-title":"Consideration of rainfall intermittency and log-normality on the merging of radar and the rain gauge rain rate","volume":"589","author":"Ro","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0123","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","article-title":"Incremental learning for robust visual tracking","volume":"77","author":"Ross","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.watres.2026.127064_bib0124","doi-asserted-by":"crossref","first-page":"473","DOI":"10.2166\/hydro.2014.063","article-title":"Analysis of extreme flooding events through a calibrated 1D\/2D coupled model: the case of Barcelona (Spain)","volume":"17","author":"Russo","year":"2015","journal-title":"J. Hydroinform."},{"key":"10.1016\/j.watres.2026.127064_bib0126","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1061\/(ASCE)1527-6988(2006)7:2(60)","article-title":"HAZUS-MH flood loss estimation methodology. I: overview and flood hazard characterization","volume":"7","author":"Scawthorn","year":"2006","journal-title":"Nat. Hazards Rev."},{"key":"10.1016\/j.watres.2026.127064_bib0127","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2025.124819","article-title":"A physically informed domain-independent data-driven inundation forecast model","volume":"289","author":"Schmid","year":"2026","journal-title":"Water Res"},{"key":"10.1016\/j.watres.2026.127064_bib0128","doi-asserted-by":"crossref","DOI":"10.1029\/2024EF004549","article-title":"National-scale flood hazard data unfit for urban risk management","volume":"12","author":"Schubert","year":"2024","journal-title":"Earth's Future"},{"key":"10.1016\/j.watres.2026.127064_bib0130","doi-asserted-by":"crossref","first-page":"683","DOI":"10.4028\/www.scientific.net\/AMM.193-194.683","article-title":"Study of simulation in storm sewer system of Zhenjiang urban by InfoWorks ICM model","volume":"193-194","author":"Sheng","year":"2012","journal-title":"Appl. Mech. Mater."},{"key":"10.1016\/j.watres.2026.127064_bib0132","doi-asserted-by":"crossref","first-page":"44","DOI":"10.3390\/earth7020044","article-title":"A comprehensive review of machine learning and deep learning methods for flood inundation mapping","volume":"7","author":"Silwal","year":"2026","journal-title":"Earth"},{"key":"10.1016\/j.watres.2026.127064_bib0133","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.3390\/s25123586","article-title":"Closing the domain gap: can pseudo-labels from synthetic UAV data enable real-world flood segmentation?","volume":"25","author":"Simantiris","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.watres.2026.127064_bib0134","doi-asserted-by":"crossref","first-page":"2635","DOI":"10.2166\/wst.2020.369","article-title":"A comprehensive review of deep learning applications in hydrology and water resources","volume":"82","author":"Sit","year":"2020","journal-title":"Water Sci. Technol."},{"key":"10.1016\/j.watres.2026.127064_bib0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2026.125799","article-title":"Enhancing cross-regional transferability of super-resolution-based flood surrogate models for data-scarce catchments","volume":"298","author":"Song","year":"2026","journal-title":"Water Res"},{"key":"10.1016\/j.watres.2026.127064_bib0136","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.watres.2026.127064_bib0137","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1111\/geoj.12103","article-title":"Improving flood forecasts for better flood preparedness in the UK (and beyond)","volume":"180","author":"Stephens","year":"2014","journal-title":"Geogr. J."},{"key":"10.1016\/j.watres.2026.127064_bib0138","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.129944","article-title":"A surrogate modeling method for distributed land surface hydrological models based on deep learning","volume":"624","author":"Sun","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0139","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.5194\/hess-29-3833-2025","article-title":"An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics","volume":"29","author":"Tan","year":"2025","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0140","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)NH.1527-6996.0000167","article-title":"Uncertainty and sensitivity analysis of the HAZUS-MH flood model","volume":"16","author":"Tate","year":"2015","journal-title":"Nat. Hazards Rev."},{"key":"10.1016\/j.watres.2026.127064_bib0141","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.envsoft.2017.01.006","article-title":"Flood inundation modelling: a review of methods, recent advances and uncertainty analysis","volume":"90","author":"Teng","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"10.1016\/j.watres.2026.127064_bib0142","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1623\/hysj.52.5.1016","article-title":"Coping with floods: preparedness, response and recovery of flood-affected residents in Germany in 2002","volume":"52","author":"Thieken","year":"2007","journal-title":"Hydrol. Sci. J."},{"key":"10.1016\/j.watres.2026.127064_bib0143","doi-asserted-by":"crossref","first-page":"3024","DOI":"10.3390\/w17203024","article-title":"A data-driven framework for flood mitigation: transformer-based damage prediction and reinforcement learning for reservoir operations","volume":"17","author":"Tofighi","year":"2025","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.127064_bib0144","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2026.135520","article-title":"Designing an inundation monitoring and real-time urban flood forecasting system: a synthetic study","volume":"674","author":"Tran","year":"2026","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2024.106132","article-title":"UIDS: a Matlab-based urban flood model considering rainfall-induced and surcharge-induced inundations","volume":"179","author":"Tran","year":"2024","journal-title":"Environ. Model. Softw."},{"key":"10.1016\/j.watres.2026.127064_bib0146","doi-asserted-by":"crossref","first-page":"3449","DOI":"10.3390\/w17243449","article-title":"Building a generalized pre-training model to predict river water-level from radar rainfall","volume":"17","author":"Ueda","year":"2025","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.127064_bib0147","doi-asserted-by":"crossref","first-page":"3263","DOI":"10.3390\/rs15133263","article-title":"Near real-time flood mapping with weakly supervised machine learning","volume":"15","author":"Vongkusolkit","year":"2023","journal-title":"Remote Sens"},{"key":"10.1016\/j.watres.2026.127064_bib0148","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.5194\/nhess-20-1149-2020","article-title":"Invited perspectives: how machine learning will change flood risk and impact assessment","volume":"20","author":"Wagenaar","year":"2020","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0149","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.5194\/nhess-17-1683-2017","article-title":"Multi-variable flood damage modelling with limited data using supervised learning approaches","volume":"17","author":"Wagenaar","year":"2017","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0150","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/nhess-16-1-2016","article-title":"Uncertainty in flood damage estimates and its potential effect on investment decisions","volume":"16","author":"Wagenaar","year":"2016","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0151","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.jcp.2019.01.031","article-title":"Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem","volume":"384","author":"Wang","year":"2019","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.watres.2026.127064_bib0152","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2022.127735","article-title":"Rapid prediction of flood inundation by interpolation between flood library maps for real-time applications","volume":"609","author":"Wang","year":"2022","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0153","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.131902","article-title":"Developing a flood risk assessment model with genetic algorithm-based weights","volume":"642","author":"Wang","year":"2024","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0154","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph192214630","article-title":"Hydrodynamic modelling and flood risk analysis of urban catchments under multiple scenarios: a case study of Dongfeng Canal district, Zhengzhou","volume":"19","author":"Wei","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.watres.2026.127064_bib0155","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"10.1016\/j.watres.2026.127064_bib0157","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1038\/s41467-020-15264-2","article-title":"New insights into US flood vulnerability revealed from flood insurance big data","volume":"11","author":"Wing","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.watres.2026.127064_bib0158","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.3390\/w17081239","article-title":"Physics-guided deep learning for spatiotemporal evolution of urban pluvial flooding","volume":"17","author":"Woo","year":"2025","journal-title":"Water"},{"key":"10.1016\/j.watres.2026.127064_bib0159","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/s13753-025-00685-8","article-title":"A ConvLSTM-based model for urban flood prediction under dynamic rainfall patterns and exploration on its extrapolation capability","volume":"16","author":"Xiao","year":"2025","journal-title":"Int. J. Disaster Risk Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.131863","article-title":"A hybrid surrogate model for real-time coastal urban flood prediction: an application to Macao","volume":"642","author":"Xu","year":"2024","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0161","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2025.133705","article-title":"Urban flood modeling and forecasting with deep neural operator and transfer learning","volume":"661","author":"Xu","year":"2025","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0163","series-title":"Proceedings of the Korea Water Resources Association Conference","first-page":"405","article-title":"The quantification of flood damage using K-FRM","author":"Yu","year":"2023"},{"key":"10.1016\/j.watres.2026.127064_bib0164","doi-asserted-by":"crossref","DOI":"10.1029\/2019WR027038","article-title":"Training machine learning surrogate models from a high-fidelity physics-based model: application for real-time street-scale flood prediction in an urban coastal community","volume":"56","author":"Zahura","year":"2020","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.watres.2026.127064_bib0165","doi-asserted-by":"crossref","first-page":"149","DOI":"10.3390\/forecast4010007","article-title":"Hybrid surrogate model for timely prediction of flash flood inundation maps caused by rapid river overflow","volume":"4","author":"Zanchetta","year":"2022","journal-title":"Forecasting"},{"key":"10.1016\/j.watres.2026.127064_bib0166","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.129499","article-title":"High temporal resolution urban flood prediction using attention-based LSTM models","volume":"620","author":"Zhang","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0167","doi-asserted-by":"crossref","first-page":"5955","DOI":"10.5194\/hess-29-5955-2025","article-title":"Deep learning of flood forecasting by considering interpretability and physical constraints","volume":"29","author":"Zhang","year":"2025","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.watres.2026.127064_bib0168","article-title":"Evaluation of emergency response capacity of urban pluvial flooding public service based on scenario simulation","volume":"19","author":"Zhang","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.watres.2026.127064_bib0169","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123137","article-title":"A hybrid method based on proper orthogonal decomposition and deep neural networks for flow and heat field reconstruction","volume":"247","author":"Zhao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.watres.2026.127064_bib0170","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.3390\/rs15092383","article-title":"Impact of urbanization on regional rainfall\u2013runoff processes: case study in Jinan City, China","volume":"15","author":"Zhao","year":"2023","journal-title":"Remote Sens."},{"key":"10.1016\/j.watres.2026.127064_bib0171","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.130475","article-title":"A study on compound flood prediction and inundation simulation under future scenarios in a coastal city","volume":"628","author":"Zhong","year":"2024","journal-title":"J. Hydrol."},{"key":"10.1016\/j.watres.2026.127064_bib0172","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2021.105112","article-title":"A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction. Environ","volume":"143","author":"Zhou","year":"2021","journal-title":"Model. Softw."}],"container-title":["Water Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0043135426017355?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0043135426017355?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T19:21:35Z","timestamp":1791746495000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0043135426017355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":157,"alternative-id":["S0043135426017355"],"URL":"https:\/\/doi.org\/10.1016\/j.watres.2026.127064","relation":{},"ISSN":["0043-1354"],"issn-type":[{"value":"0043-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing urban flood risk assessment: A PCA-integrated deep learning surrogate for hazard and damage prediction","name":"articletitle","label":"Article Title"},{"value":"Water Research","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.watres.2026.127064","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"127064"}}