Articles | Volume 26, issue 8
https://doi.org/10.5194/nhess-26-3943-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-3943-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Aug 2026
Research article |
| 20 Aug 2026
| 20 Aug 2026
Using machine learning for the prediction of flood-related 112 calls
AIWELL Lab, Universitat Oberta de Catalunya, Rambla del Poblenou, 154, 08018 Barcelona, Spain
Hydrometeorological Innovative Solutions (HYDS), Jordi Girona 1–3, ParcUPC K2M, 08034 Barcelona, Spain
Andreas Kaltenbrunner
Department of Engineering, Universitat Pompeu Fabra, Carrer de Roc Boronat 138, 08018 Barcelona, Spain
Àgata Lapedriza
AIWELL Lab, Universitat Oberta de Catalunya, Rambla del Poblenou, 154, 08018 Barcelona, Spain
Institute for Experiential AI, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
Xavier Llort
Hydrometeorological Innovative Solutions (HYDS), Jordi Girona 1–3, ParcUPC K2M, 08034 Barcelona, Spain
Cited articles
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, https://doi.org/10.1145/3292500.3330701, 2019. a
Alcântara, E., Guimarães, Y. C., Baião, C. F. P., and Mantovani, J. R.: Interpretable machine learning for flood susceptibility mapping in the metropolitan region of São Paulo, Southeast Brazil, Discov. Geosci., 3, 243, https://doi.org/10.1007/s44288-025-00362-9, 2025. a
Alet, F., Price, I., El-Kadi, A., Masters, D., Markou, S., Andersson, T. R., Stott, J., Lam, R., Willson, M., Sanchez-Gonzalez, A., and Battaglia, P.: Skillful joint probabilistic weather forecasting from marginals, arXiv [preprint], https://doi.org/10.48550/arXiv.2506.10772, 2025. a
Alfieri, L. and Thielen, J.: A European Precipitation Index for Extreme Rain-Storm and Flash Flood Early Warning, Meteorol. Appl., 22, 3–13, https://doi.org/10.1002/met.1328, 2012. a
Barnolas, M. and Llasat, M. C.: A flood geodatabase and its climatological applications: the case of Catalonia for the last century, Nat. Hazards Earth Syst. Sci., 7, 271–281, https://doi.org/10.5194/nhess-7-271-2007, 2007. a
Berenguer, M., Corral, C., Sánchez-Diezma, R., and Sempere-Torres, D.: Hydrological Validation of a Radar-Based Nowcasting Technique, J. Hydrometeorol., 6, 532–549, https://doi.org/10.1175/jhm433.1, 2005. a
Berenguer, M., Sempere-Torres, D., and Pegram, G. G.: SBMcast – An ensemble nowcasting technique to assess the uncertainty in rainfall forecasts by Lagrangian extrapolation, J. Hydrol., 404, 226–240, https://doi.org/10.1016/j.jhydrol.2011.04.033, 2011. a
Berenguer, M., Park, S., Baugh, C., O'Regan, K., Prudhomme, C., Pulkkinen, S., Myllykoski, H., Santiago, A., Durán, A. M., Torres, R. M., Vara, M., Gomes, A., Pereira Colonese, J., and Tasev, D.: Early warning Demonstration of pan-European rainfall-induced impact forecasts – the EDERA project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17162, https://doi.org/10.5194/egusphere-egu24-17162, 2024. a
Brousse, O., Simpson, C. H., Poorthuis, A., and Heaviside, C.: Unequal distributions of crowdsourced weather data in England and Wales, Nat. Commun., 15, 4828–4811, https://doi.org/10.1038/s41467-024-49276-z, 2024. a
Camarasa-Belmonte, A. M. and López, M. P. C.: Luvias in situ en la comunidad valenciana. Relación entre indicadores pluviométricos, llamadas al centro de coordinación de emergencias (112) y relación de daños, durante el episodio de 26–30 de noviembre de 2016, in: Publicaciones de la Asociación Española de Climatología. Serie A; 11, XI Congreso de la Asociación Española de Climatología, Cartagena, España, 17–19 October 2018, http://hdl.handle.net/20.500.11765/9904 (last access: 27 November 2025), 2018. a
Cartographic and Geological Institute of Catalonia (ICGC): Home Page – Dataset for the Land Cover Map, https://www.icgc.cat/en/Geoinformation-and-Maps/Maps/Dataset-Land-cover-map-CatLC (last access: 1 July 2025), 2021a. a
Cartographic and Geological Institute of Catalonia (ICGC): CatLC Github Page, https://github.com/OpenICGC/CatLC?tab=readme-ov-file (last access: 1 July 2025), 2021b. a
Cartographic and Geological Institute of Catalonia (ICGC): Land Cover, https://www.icgc.cat/en/Sustainable-territory/Land-cover (last access: 27 May 2025), 2022. a
Cartographic and Geological Institute of Catalonia (ICGC): Administrative Divisions, https://www.icgc.cat/ca/Geoinformacio-i-mapes/Dades-i-productes/Geoinformacio-cartografica/Divisions-administratives (last access: 1 April 2025), 2024. a
Cartographic and Geological Institute of Catalonia (ICGC): Home Page – 5×5 m Terrain Elevation Model, https://www.icgc.cat/en/Geoinformation-and-Maps/Data-and-products/Bessons-digitals-Elevacions/5x5-m-Terrain-elevation-model (last access: 2 February 2026), 2025. a
Cascante, M.: El pantà de Sau, a mínims del segle XXI i ja per sota del 10 %, Betevé, https://beteve.cat/medi-ambient/panta-sau-minims-segle-xxi-marc-2023 (last access: 20 May 2025), 14 March 2023, 2023. a
Chen, K., Han, T., Gong, J., Bai, L., Ling, F., Luo, J.-J., Chen, X., Ma, L., Zhang, T., Su, R., Ci, Y., Li, B., Yang, X., and Ouyang, W.: FengWu: Pushing the Skillful Global Medium-Range Weather Forecast beyond 10 Days Lead, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.02948, 2023. a
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, p. 785–794, San Francisco, California, USA, https://doi.org/10.1145/2939672.2939785, 2016. a
Collier, C. G.: Flash flood forecasting: What are the limits of predictability?, Q. J. R. Meteorol. Soc., 133, 3–23, https://doi.org/10.1002/qj.29, 2007. a
Corral, C., Berenguer, M., Sempere-Torres, D., Poletti, L., Silvestro, F., and Rebora, N.: Comparison of Two Early Warning Systems for Regional Flash Flood Hazard Forecasting, J. Hydrol., 572, 603–619, https://doi.org/10.1016/j.jhydrol.2019.03.026, 2019. a, b
Dalmau, R., Attia, J., and Gawinowski, G.: Modelling the Impact of Adverse Weather on Airport Peak Service Rate with Machine Learning, Atmosphere, 14, 1476, https://doi.org/10.3390/atmos14101476, 2023. a
Diboulo, E., Sié, A., Rocklöv, J., Niamba, L., Yé, M., Bagagnan, C., and Sauerborn, R.: Weather and mortality: a 10 year retrospective analysis of the Nouna Health and Demographic Surveillance System, Burkina Faso, Glob. Health Action, 5, 19078–13, https://doi.org/10.3402/gha.v5i0.19078, 2012. a
EDERA Consortium: EDERA: Early warning Demonstration of pan-European rainfall-induced impact forecasts, https://edera-project.eu/ (last access: 5 February 2026), 2025. a
EUMETNET: MeteoAlarm project, https://www.meteoalarm.org (last access: 7 July 2025), 2025. a
Eurogeographics: EuroDEM Digital Elevation Model Dataset, https://www.mapsforeurope.org/datasets/euro-dem (last access: 1 April 2025), 2024. a
European Commission: European Flood Awareness System (EFAS), https://european-flood.emergency.copernicus.eu/en (last access: 5 February 2026), 2026. a
European Parliament and Council of the European Union: Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE), Directive, Directorate-General for Environment, http://data.europa.eu/eli/dir/2007/2/oj (last access: 17 January 2026), 2007. a
Gao, S. and Wang, Y.: Explainable deep learning powered building risk assessment model for proactive hurricane response, Risk Anal., 43, 1222–1234, https://doi.org/10.1111/risa.13990, 2023. a
Gaztelumendi, S., Egaña, J., Ruiz, M., and Iturrioz, E.: The Basque Impact Weather Catalogue, J. Adv. Sci. Res., 21, 41–48, https://doi.org/10.5194/asr-21-41-2024, 2024. a, b
General Directorate of Catalonia Civil Protection: Pla Territorial de Protecció Civil de Catalunya (PROCICAT), Barcelona, https://dsp.interior.gencat.cat/handle/20.500.14007/2830 (last access: 15 September 2025), 2022. a
Giner Pérez De Lucia, J., López-Ballesteros, A., Fernández-Pedauyé, J., Senent-Aparicio, J., and Cecilia, J. M.: Harnessing social sensing for real-time flood event reconstruction: A digital autopsy of the 2024 Valencia DANA, Int. J. Disaster Risk Sci., 132, 105966, https://doi.org/10.1016/j.ijdrr.2025.105966, 2026. a
Harrison, S. E., Potter, S. H., Prasanna, R., Doyle, E. E. H., and Johnston, D.: Identifying the Impact-Related Data Uses and Gaps for Hydrometeorological Impact Forecasts and Warnings, Weather Clim. Soc., 14, 155–176, https://doi.org/10.1175/WCAS-D-21-0093.1, 2022. a, b
Iglesias, J., Cuesta, I., Salueña, C., Solé, J., Prevatt, D. O., and Fabregat, A.: Predictive modeling of severe weather impact on individuals and populations using Machine Learning, Int. J. Disaster Risk Sci., 105, 104398, https://doi.org/10.1016/j.ijdrr.2024.104398, 2024. a, b
Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., Ndirangu, P., Paolotti, D., Teklesadik, A., and Turner, M. L.: Towards a global impact-based forecasting model for tropical cyclones, Nat. Hazards Earth Syst. Sci., 24, 309–329, https://doi.org/10.5194/nhess-24-309-2024, 2024. a, b
Kox, T., Harrison, S., Ziegler, F., and Gerhold, L.: Perceptions, hopes, and concerns regarding the possibilities of artificial intelligence in weather warning contexts, Int. J. Disaster Risk Sci., 130, 105817, https://doi.org/10.1016/j.ijdrr.2025.105817, 2025. a, b
Kuglitsch, M. M., Pelivan, I., Ceola, S., Menon, M., and Xoplaki, E.: Facilitating adoption of AI in natural disaster management through collaboration, Nat. Commun., 13, 1579, https://doi.org/10.1038/s41467-022-29285-6, 2022. a
Lundberg, S. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, arXiv [preprint], https://doi.org/10.48550/arXiv.1705.07874, 2017. a, b
Láng-Ritter, J., Berenguer, M., Dottori, F., Kalas, M., and Sempere-Torres, D.: Compound flood impact forecasting: integrating fluvial and flash flood impact assessments into a unified system, Hydrol. Earth Syst. Sci., 26, 689–709, https://doi.org/10.5194/hess-26-689-2022, 2022. a
Meléndez-Landaverde, E. R. and Sempere-Torres, D.: Design and Evaluation of a Community and Impact-Based Site-Specific Early Warning System (SS-EWS): The SS-EWS Framework, J. Flood Risk Manag., 18, e12860, https://doi.org/10.1111/jfr3.12860, 2025. a
Merz, B., Blöschl, G., Vorogushyn, S., Dottori, F., Aerts, J. C. J. H., Bates, P., Bertola, M., Kemter, M., Kreibich, H., Lall, U., and Macdonald, E.: Causes, Impacts and Patterns of Disastrous River Floods, Nat. Rev. Earth Environ., 2, 592–609, https://doi.org/10.1038/s43017-021-00195-3, 2021. a
Ministry for Ecological Transition and the Demographic Challenge: Flood Maps, https://www.miteco.gob.es/en/cartografia-y-sig/ide/descargas/agua/zi-lamina.html (last access: 27 May 2025), 2024. a
National Institute of Statistics: Annual Population Census. Population by municipality and gender., https://www.ine.es/jaxiT3/Tabla.htm?t=68065&L=1 (last access: 29 May 2025), 2024. a
Nguyen, T., Shah, R., Bansal, H., Arcomano, T., Maulik, R., Kotamarthi, V., Foster, I., Madireddy, S., and Grover, A.: Scaling Transformer Neural Networks for Skillful and Reliable Medium-Range Weather Forecasting, arXiv [preprint], https://doi.org/10.48550/arXiv.2312.03876, 2024. a
Niemi, T., Baugh, C., Berenguer, M., Berruezo, A., Leinonen, M., von Lerber, A., Park, S., Prudhomme, C., Pulkkinen, S., and Ritvanen, J.: Advanced Tools for pro-Active Management of Impacts and Risks Induced by Convective Weather, Heavy Rain and Flash floods in Europe – TAMIR project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8194, https://doi.org/10.5194/egusphere-egu21-8194, 2021. a
Oliva, A. and Olcina, J.: Floods and Emergency Management: Elaboration of Integral Flood Maps Based on Emergency Calls (112) – Episode of September 2019 (Vega Baja del Segura, Alicante, Spain), Water, 15, 2, https://doi.org/10.3390/w15010002, 2022. a
Ortiz, G., Aznar-Crespo, P., Oliva, A., Olcina-Cantos, J., and Aledo, A.: Uses and opportunities of emergency calls as a resource for flood risk management, Int. J. Disaster Risk Sci., 100, 104160, https://doi.org/10.1016/j.ijdrr.2023.104160, 2024. a
Papagiannaki, K., Kotroni, V., and Lagouvardos, K.: Impact-Based Analysis of Weather-Related Hazards in Greece (2000–2025): Insights from the High-Impact Weather Events Database (HIWE-DB), Climate, 14, 105, https://doi.org/10.3390/cli14050105, 2026. a
Park, S., Berenguer, M., and Sempere-Torres, D.: Long-Term Analysis of Gauge-Adjusted Radar Rainfall Accumulations at European Scale, J. Hydrometeorol., 573, 768–777, https://doi.org/10.1016/j.jhydrol.2019.03.093, 2019. a, b
Pesaresi, M. and Politis, P.: GHS-BUILT-H R2023A - GHS building height, derived from AW3D30, SRTM30, and Sentinel2 composite, European Commission, Joint Research Centre (JRC) [data set], https://doi.org/10.2905/85005901-3A49-48DD-9D19-6261354F56FE, 2018. a
Pesaresi, M., Schiavina, M., Politis, P., Freire, S., Krasnodębska, K., Uhl, J. H., Carioli, A., Corbane, C., Dijkstra, L., Florio, P., Friedrich, H. K., Gao, J., Leyk, S., Lu, L., Maffenini, L., Mari-Rivero, I., Melchiorri, M., Syrris, V., Van Den Hoek, J., and Kemper, T.: Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data, Int. J. Digit. Earth, 17, https://doi.org/10.1080/17538947.2024.2390454, 2024. a
Potter, S., Kox, T., Mills, B., Taylor, A., Robbins, J., Cerrudo, C., Wyatt, F., Harrison, S., Golding, B., Lang, W., Harris, A. J., Kaltenberger, R., Kienberger, S., Brooks, H., and Tupper, A.: Research gaps and challenges for impact-based forecasts and warnings: Results of international workshops for High Impact Weather in 2022, Int. J. Disaster Risk Sci., 118, 105234, https://doi.org/10.1016/j.ijdrr.2025.105234, 2025. a
Price, I., Sanchez-Gonzalez, A., Alet, F., Andersson, T. R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., Lam, R., and Willson, M.: Probabilistic Weather Forecasting with Machine Learning, Nature, 637, 84–90, https://doi.org/10.1038/s41586-024-08252-9, 2025. a
Raynaud, D., Thielen, J., Salamon, P., Burek, P., Anquetin, S., and Alfieri, L.: A Dynamic Runoff Co-Efficient to Improve Flash Flood Early Warning in Europe: Evaluation on the 2013 Central European Floods in Germany, Meteorol. Appl., 22, 410–418, https://doi.org/10.1002/met.1469, 2015. a
Ribeiro, M. T., Singh, S., and Guestrin, C.: ”Why Should I Trust You?”: Explaining the Predictions of Any Classifier, in: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144, https://doi.org/10.1145/2939672.2939778, 2016. a
Ritter, J., Berenguer, M., Corral, C., Park, S., and Sempere-Torres, D.: ReAFFIRM: Real-time Assessment of Flash Flood Impacts – A Regional High-Resolution Method, Environ. Int., 136, 105375, https://doi.org/10.1016/j.envint.2019.105375, 2020. a
Ritter, J., Berenguer, M., Park, S., and Sempere-Torres, D.: Real-time Assessment of Flash Flood Impacts at Pan-European Scale: The ReAFFINE Method, J. Hydrol., 603, 127022, https://doi.org/10.1016/j.jhydrol.2021.127022, 2021. a
Rossi, P. J., Hasu, V., Halmevaara, K., Mäkelä, A., Koistinen, J., and Pohjola, H.: Real-Time Hazard Approximation of Long-Lasting Convective Storms Using Emergency Data, J. Atmos. Ocean. Technol., 30, 538–555, https://doi.org/10.1175/JTECH-D-11-00106.1, 2013. a
Sangkharat, K., Thornes, J. E., Wachiradilok, P., and Pope, F. D.: Determination of the impact of rainfall on road accidents in Thailand, Heliyon, 7, e06061, https://doi.org/10.1016/j.heliyon.2021.e06061, 2021. a
Schroeter, S., Richter, H., Arthur, C., Wilke, D., Dunford, M., Wehner, M., and Ebert, E.: Forecasting the Impacts of Severe Weather, Aust. J. Emerg. Manag., pp. 76–83, https://doi.org/10.47389/36.1.76, 2021. a, b
Schultz, M., Reitmann, S., and Alam, S.: Predictive classification and understanding of weather impact on airport performance through machine learning, Transp. Res. Part C Emerg. Technol., 131, 103119, https://doi.org/10.1016/j.trc.2021.103119, 2021. a
Schuster, S. S., Blong, R. J., Leigh, R. J., and McAneney, K. J.: Characteristics of the 14 April 1999 Sydney hailstorm based on ground observations, weather radar, insurance data and emergency calls, Nat. Hazards Earth Syst. Sci., 5, 613–620, https://doi.org/10.5194/nhess-5-613-2005, 2005. a
Scientific Advice Mechanism to the European Commission: Artificial Intelligence in Emergency and Crisis Management: Rapid Evidence Review Report, Tech. rep., SAPEA, Zenodo, https://doi.org/10.5281/ZENODO.17737962, 2025. a
Sirenko, M., Comes, T., and Verbraeck, A.: The rhythm of risk: Exploring spatio-temporal patterns of urban vulnerability with ambulance calls data, Environ. Plan. B Urban Anal. City Sci., 52, 863–881, https://doi.org/10.1177/23998083241272095, 2025. a
Spanish National Meteorological Agency (AEMET): Home Page – AEMET OpenData, https://opendata.aemet.es/centrodedescargas/inicio (last access: 19 August 2025), 2016. a
Spanish National Meteorological Agency (AEMET): Informe sobre el episodio meteorológico de precipitaciones torrenciales y persistentes ocasionadas por una DANA el día 29 de octubre de 2024, Tech. rep., Ministry for Ecological Transition and the Demographic Challenge, Madrid, Spain, http://hdl.handle.net/20.500.11765/16360 (last access: 4 February 2026), 2024. a
Stanski, H. R., Wilson, L., and Burrows, W.: Survey of Common Verification Methods in Meteorology, Tech. rep., World Meteorological Organization, Ontario, Canada, https://www.cawcr.gov.au/projects/verification/Stanski_et_al/Stanski_et_al.html (last access: 4 February 2026), 1989. a
Statistical Institute of Catalonia: Population as of 1 January. Counties and Aran, areas and provinces, https://www.idescat.cat/indicadors/?id=aec&n=15224&lang=en&t=202400 (last access: 28 May 2025), 2024. a
Teklesadik, A. and van den Homberg, M.: Forecasting impacts of tropical cyclones with machine learning : A case study in the Philippines , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12917, https://doi.org/10.5194/egusphere-egu22-12917, 2022. a, b
Terti, G., Ruin, I., Gourley, J. J., Kirstetter, P., Flamig, Z., Blanchet, J., Arthur, A., and Anquetin, S.: Toward Probabilistic Prediction of Flash Flood Human Impacts, Risk Anal., 39, 140–161, https://doi.org/10.1111/risa.12921, 2019. a
Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A.: The European Flood Alert System – Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125–140, https://doi.org/10.5194/hess-13-125-2009, 2009. a
United Nations Advisory Body on Artificial Intelligence: Govering AI for humanity: final report, Tech. rep., United Nations, New York, NY, USA, http://digitallibrary.un.org/record/4062495 (last access: 20 December 2025), 2024. a
United Nations Office for Disaster Risk Reduction: Global Assessment Report on Disaster Risk Reduction (GAR) 2015: Making Development Sustainable: The Future of Disaster Risk Management, Tech. rep., United Nations Office for Disaster Risk Reduction (UNDRR), Geneva, Switzerland, https://www.undrr.org/quick/11404 (last access: 14 April 2025), 2015a. a
United Nations Office for Disaster Risk Reduction: Sendai Framework for Disaster Risk Reduction 2015–2030, Tech. rep., United Nations Office for Disaster Risk Reduction (UNDRR), Geneva, Switzerland, https://www.undrr.org/quick/11409 (last access: 28 February 2025), 2015b. a
Van Den Homberg, M. J. C., Gevaert, C. M., and Georgiadou, Y.: The Changing Face of Accountability in Humanitarianism: Using Artificial Intelligence for Anticipatory Action, Politics Gov., 8, 456–467, https://doi.org/10.17645/pag.v8i4.3158, 2020. a
Wagenaar, D., Hermawan, T., van den Homberg, M. J. C., Aerts, J. C. J. H., Kreibich, H., de Moel, H., and Bouwer, L. M.: Improved Transferability of Data-Driven Damage Models Through Sample Selection Bias Correction, Risk Anal., 41, 37–55, https://doi.org/10.1111/risa.13575, 2021. a
Zheng, J., Fang, W., and Shao, J.: A Novel Framework for Multi-Hazard Loss Assessment of Tropical Cyclones: A County-Level Interpretable Machine Learning Model, Int. J. Disaster Risk Sci., 117, 105204, https://doi.org/10.1016/j.ijdrr.2025.105204, 2025. a
Zhou, S.: Application of AI in Urban Flash Flood Risk Assessment: From Real-time Warning to Resilience Planning, Appl. Comput. Eng., 150, 9–14, https://doi.org/10.54254/2755-2721/2025.22398, 2025. a
Short summary
In this work, we employ machine learning (ML) techniques to develop models combining meteorological data, population characteristics, and historical 112 call records to predict which municipalities will report emergencies within the next hour. Compared to operational, hazard-based systems, our approach demonstrates a substantial improvement, particularly in moderately to highly populated areas. This highlights the potential for ML to provide timely, localized anticipation of flood impacts.
In this work, we employ machine learning (ML) techniques to develop models combining...
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