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Abstract: Integrating Hydrodynamic Modeling, Remote Sensing, and Machine Learning for Urban Flood Simulation and Prediction in New York City (AGU25)

Integrating Hydrodynamic Modeling, Remote Sensing, and Machine Learning for Urban Flood Simulation and Prediction in New York City

Shima Kamali, CUNY City College of New York, New York, NY, United States and Reza Khanbilvardi, Department of Civil Engineering, CUNY-CREST Institute, and United Nations University (UNU) Hub on Remote-Sensing and Sustainable Innovations for Resilient Urban Systems (R-SIRUS)-UNU Institute for Water, Environment and Health (UNU-INWEH), The City College of New York, The City University of New York, New York, United States
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