Hydrology and flood forecasting are critical areas of research and application, especially in the face of increasing climate variability and change. Flooding impacts communities, economies, and ecosystems, causing widespread damage and posing significant challenges for water resource management, urban planning, and disaster response. Advanced tools and techniques are needed to enhance our ability to predict, mitigate, and manage flood risks. This session invites research and applications of AI to address the multi-disciplinary challenges in hydrology and flood forecasting. Topics could include, but are not limited to, real-time flood forecasting and early warning systems, hydrological modeling and simulation, extreme weather and precipitation prediction, risk assessment and management, using AI for impact analysis and adaptation strategies, innovative monitoring and sensing technologies (eg IoT devices and advanced sensor networks).

Researchers are encouraged to submit talks that highlight novel AI methodologies, case studies, and practical applications in hydrology and flood forecasting. Contributions that demonstrate the interdisciplinary nature of this field, combining insights from meteorology, hydrology and data science, are particularly welcome.

Papers:

8:30 AM

J1B.1

Towards Improving a Machine Learning Model for Excessive Rainfall in the Southwest United States

Jacob A Escobedo, Colorado State Univ., Fort Collins, CO; and R. S. Schumacher

8:45 AM

J1B.2

Evaluation of a Machine Learning Postprocessor to Mitigate QPF Errors for Mesoscale Convective Systems

Tyreek JaQuan Frazier, Iowa State University, Ames, IA; Iowa State University, Ames, IA; and A. Pathak, W. A. Gallus Jr., S. Dutta, and K. J. Franz

J1B.3

Improving the Predictability of Heavy Precipitation Events during the Nowcast Period using Convolutional Neural Networks

9:15 AM

J1B.4

Using Machine Learning and Geospatial Data to Predict Groundwater Occurrence of Arsenic in the Colorado Plateau

Austin Joseph Martinez, California State University, Dominguez Hills, Salinas, CA

Handout (3.1 MB)

9:30 AM

J1B.5

Large-sample CONUS-scale predictions of water quality: datasets and learning frameworks

Shuyu Chang, Penn State University, State College, PA; and D. Aboelyazeed, K. A. Sawadekar, D. Chavada, C. Shen, and K. Van Meter

9:45 AM

J1B.6

Causal Machine Learning for predicting Flood induced residential property damage in urban coastal regions

Mujungu Lawrence Museru, The University of Memphis, Memphis, TN; and R. Nazari and M. Karimi

Recorded Presentation

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