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Hamid Moradkhani
The University of AlabamaMeeting roles in:
A Deep Learning Approach for Real-Time Urban Flood Inundation Forecasting at Metropolitan Scale
Advancing the Use of Hydroclimatic Forecasts for Water Resources Decision-Making I Poster
A hierarchical Bayesian approach to understanding recent flash flood trends in the USA
Enhanced Cumulative Likelihood of Potential Impacts using synthetic tropical cyclones for coastal flooding, a case study applied to Mobile, Alabama
Spatial Delineation of the Compound Flood Transition Zone Using DeepLearning
Advancing the Use of Hydroclimatic Forecasts for Water Resources Decision-Making II Oral
GEE-FMF: A Google Earth Engine-Based Machine Learning Framework for Efficient Regional Flood Mapping
A Spatio-Temporal Bayesian Hierarchical Model for Estimating Precipitation Extremes at Unmonitored Gauges Across the CONUS
Advancing Physics-Informed Compound Flood Modeling through a Newton-Guided Deep Learning Model
Enhancing Compound Flood Forecasting Mapping Through Multi-Model Atmospheric Forcing and Machine Learning-Based Boundary Conditions
Machine Learning Based Dynamic Integration of Grace-Based Water Storage into Global Hydrological Models
A Multi-Dimensional Assessment of Energy Security Across the United States
Assessing Drought Impacts on Agricultural Systems Through a Water-Energy-Food Nexus Lens in the U.S. Deep South
Assessing Future Storm Surge Hazards on U.S. Coasts Through Statistical Downscaling of Climate Projections
Standardizing Performance Evaluation for Compound Flooding: Challenges and Recommendations
Machine Learning and Data Assimilation for Terrestrial Hydrologic Modeling and Discovery I Poster
Coping with Data Scarcity in Extreme Flood Forecasting: A Deep Generative Modeling Approach
Understanding Simulation Uncertainty in Compound Floods: Sensitivity to Forcing and Model Parameters
Explainable Deep Transfer Learning for Spatiotemporal Runoff Prediction at the Continental Scale
An Integrated Framework Towards Strengthening Transportation Infrastructure Resilience to Compound Flooding Impacts
Machine Learning and Data Assimilation for Terrestrial Hydrologic Modeling and Discovery II Oral
Generative postprocessing of HRRR QPF for improved flash-flood forecasting
Baseflow Data Assimilation to Improve Hydrologic Modeling through Enhanced Representation of Groundwater–Surface-Water Interactions
Machine Learning and Data Assimilation for Terrestrial Hydrologic Modeling and Discovery III Oral
Enhancing Physics-Based Streamflow Prediction through Evolutionary Particle Filter Data Assimilation
Integrated Hydrologic-Routing Data Assimilation in a Semi-Distributed Model for Enhanced Streamflow Prediction
NLP and BERT Deep Learning-based Approaches for Improved Impact-based Forecasting and Disaster Management
GEE-FMF: A Google Earth Engine-Based Machine Learning Framework for Efficient Regional Flood Mapping
Understanding Simulation Uncertainty in Compound Floods: Sensitivity to Forcing and Model Parameters
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