IN009
Advancing Urban Risk Modeling: From Physics Foundations to AI Innovations
Session ID#: 250407
Session Description:
Modeling climate hazards in urban environments requires advanced expertise, significant computational resources, and access to localized data—factors that can make it prohibitively expensive and inaccessible for many communities. These models often rely on high-emission computing workflows and are not always designed to produce actionable insights. To drive effective resilience and adaptation strategies, modeling frameworks must not only integrate physics-based and AI approaches but also be scalable, low-emission, and embedded within practical risk assessment and decision-making processes.This session welcomes contributions for novel methods to leverage machine learning for extreme heat and urban flood modeling (pluvial, fluvial, and coastal); multi-hazard and compounding risk assessment in urban systems; and innovative data processing and computational techniques that address scalability and challenges with natural hazard modeling.
Index Terms:
1942 Machine learning [INFORMATICS]
1952 Modeling [INFORMATICS]
4301 Atmospheric [NATURAL HAZARDS]
4303 Hydrological [NATURAL HAZARDS]
Primary Convener: Ahmed Mustafa, Urban Systems Lab, New York University, New York, United States and Rajan Jain, Anna Kramer
Conveners: Timon McPhearson, Urban Systems Lab, The New School, New York, United States, Ashish Shrestha, Urban Systems Lab, The New School, New Yotk, United States, Madhavi Jain, New York University, Urban Systems Lab, Environmental Studies Department, New York, United States and Rajan Jain, Anna Kramer
Student/Early Career Convener: Sally El Hajjar, The New School, Urban Systems Lab, New York, United States and Rajan Jain, Anna Kramer
Towards a multi-sensor satellite AI database of observed U.S. flood extents 2001–2024 (Invited) (1855451)
Seth Bryant1,2, Dr. Saurabh Kaushik, PhD1, Jonathan Sullivan1 and Beth Tellman1,3, (1)University of Wisconsin Madison, Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, Madison, United States, (2)University of Arizona, School of Geography, Development & Environment, Tucson, United States, (3)University of Arizona, School of Geography, Development and Environment, Tucson, United States
Synthetic drainage solutions for data-scarce urban environments (Invited) (1859624)
Christos Iliadis1,2 and Lampros Vasiliades1, (1)University of Thessaly, Department of Civil Engineering, School of Engineering, Volos, Greece, (2)Newcastle University, School of Engineering, Tyne and Wear, United Kingdom
AI for Urban Floods: Real-World Scaling of a ConvLSTM-based Framework (1869642)
Sally El Hajjar1, Ahmed Mustafa2, Rajan Jain3, Ashish Shrestha1, Madhavi Jain4, Christos Iliadis5, Vassilis Glenis6 and Timon McPhearson2, (1)New York University, Environmental Studies Department, New York, United States, (2)New York University, Urban Systems Lab, Department of Environmental Studies, New York City, United States, (3)New York University, Environmental Studies Department, New York, NY, United States, (4)New York University, Urban Systems Lab, Environmental Studies Department, New York, United States, (5)University of Thessaly, Department of Civil Engineering, School of Engineering, Volos, Greece, (6)Newcastle University, Newcastle Upon Tyne, United Kingdom
The EMERGENCIES Project or a Forward-Looking High-Resolution Modeling and Decision-Support System in Case of Hazardous Atmospheric Releases (1877997)
Patrick Pierre Armand, CEA Commissariat à l'Energie Atomique DAM, Arpajon Cedex, France, Christophe Duchenne, CEA Commissariat à l'Energie Atomique DAM, Arpajon, France, Olivier Oldrini, MOKILI, Paris, France and Sylvie Perdriel, AmpliSIM, Paris, France
uMORPH: A High Resolution Global Urban Morphology Dataset for Urban Numerical Weather Prediction (1880661)
Madhavi Jain, New York University, Urban Systems Lab, Environmental Studies Department, New York, United States, Luis Enrique Ortiz, George Mason University, Virginia Climate Center, Fairfax, United States and Timon McPhearson, New York University, Urban Systems Lab, Department of Environmental Studies, New York City, United States
Enhanced Forecasting Methods for Extreme Weather Events and Vulnerable Populations (1883464)
Nadia Ahmad1, Tarek Kandakji2, Luke Sanford3, Dan Esty4, Gerald Torres4, Zhe Zhu5 and Katharine J Mach6, (1)Yale University, School of the Environment, New Haven, United States, (2)Baylor College of Medicine, Houston, United States, (3)Yale University, School of Environment, New Haven, CT, United States, (4)Yale University, New Haven, United States, (5)University of Connecticut, Department of Natural Resources and the Environment, Storrs, United States, (6)Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Department of Environmental Science and Policy, Miami, United States
Statistical Characterization and Modeling of Climate Data in a Desert Urban Environment in Phoenix, AZ (1888578)
Aashish Aryal, Arizona State University, Tempe, AZ, United States and Giuseppe Mascaro, Arizona State University, School of Sustainable Engineering and the Built Environment, Tempe, AZ, United States
From Weeks to Seconds: ClimateIQ’s ML Engine Redefines Flood and Heat Modeling (1890727)
Ahmed Mustafa1, Christopher Kenedy2, Anna Kramer3, Luis Enrique Ortiz4, Sally El Hajjar3, Madhavi Jain5, Ashish Shrestha6, Rajan Jain7, Joseph Steele1 and Timon McPhearson8, (1)Urban Systems Lab, New York University, New York, United States, (2)New York University, Urban Systems Lab, New York, United States, (3)The New School, Urban Systems Lab, New York, United States, (4)George Mason University, Virginia Climate Center, Fairfax, United States, (5)New York University, Urban Systems Lab, Environmental Studies Department, New York, United States, (6)New York University, Environmental Studies Department, New York, United States, (7)New York University, Environmental Studies Department, New York, NY, United States, (8)New York University, Urban Systems Lab, Department of Environmental Studies, New York City, United States
Architecting ClimateIQ’s Frontend: Delivering High-Resolution Hazard Data Through Scalable, User-Centered Interfaces (1896188)
Rajan Jain1, Joseph Steele2, Ahmed Mustafa3, Sally El Hajjar4, Madhavi Jain5, Ashish Shrestha6, Christopher Kenedy7 and Timon McPhearson3, (1)New York University, Environmental Studies Department, New York, NY, United States, (2)Urban Systems Lab, New York University, New York, United States, (3)New York University, Urban Systems Lab, Department of Environmental Studies, New York City, United States, (4)The New School, Urban Systems Lab, New York, United States, (5)New York University, Urban Systems Lab, Environmental Studies Department, New York, United States, (6)New York University, Environmental Studies Department, New York, United States, (7)New York University, Urban Systems Lab, New York, United States
Risk Assessment of Air Pollution Based on a New Clustering Approach: A Scalable and Low-Emission Framework for Urban Climate Hazard Modeling (1947152)
Hassan Kassem1, Sally El Hajjar2, Ahmed Mustafa3, Timon McPhearson3 and Hichem Omrani4, (1)University of Luxembourg, Luxembourg, Luxembourg, (2)The New School, Urban Systems Lab, New York, United States, (3)New York University, Urban Systems Lab, Department of Environmental Studies, New York City, United States, (4)Luxembourg Institute of Socio-Economic Research - LISER, UDM, Belval, Luxembourg
Connectivity-Informed Siting of Urban Stormwater Control Measures: The HydroDisconnect Approach (1968781)
Francesco Dell'Aira1, Mr. Nischal Kafle1, Jon Hathaway2 and Claudio I Meier1, (1)University of Memphis, Department of Civil Engineering, Memphis, TN, United States, (2)University of Tennessee Knoxville, Knoxville, United States
