Meeting roles in:
Evaluating the Reliability of Flood Inundation Mapping in Ungauged Locations Using Observed, Modeled, and Machine Learning-Based Hydrologic Data
Using Machine Learning to Diagnose and Bias-Correct NextGen Framework Limitations
Cloud Dynamics to Soil Moisture: AI-Enabled Shortcuts in Hydrologic Prediction
Leveraging Large-Scale Meteorological Geospatial Information to Predict Snow Water Equivalent with Machine Learning
Streamlining Community Modeling through Automated Data Processing for the Next Generation Water Resources Modeling Framework
A Tiled Calibration Framework for Hydrological Models Using Land Use and Land Cover Variability
Savalan is a Ph.D. student in the Civil, Construction, and Environmental Engineering Department at The University of Alabama. He currently works as a graduate research assistant at the Cooperative Institute for Research to Operations in Hydrology (CIROH). His research focuses on applying Al in streamflow prediction, improving the National Water Model (NWM) predictions, evaluating hydrological droughts, and analyzing water data. His work aims to develop innovative solutions to improve water resource management and operational hydrological prediction.