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Jonathan Frame
University of AlabamaMeeting roles in:
Explaining and Predicting the Model Performance via Convex Hull Analysis of Internal States of LSTM based NWM Surrogate
Advancing Prediction, Theory, and Causal Understanding in Geosciences Through AI and Big Data II Oral
Explaining and Predicting the Model Performance via Convex Hull Analysis of Internal States of LSTM based NWM Surrogate
Advancing Prediction, Theory, and Causal Understanding in Geosciences Through AI and Big Data III Poster
Demonstrating the Feasibility of DL-Based Pluvial Flood Mapping in Urban Settings
Extracting Hydrological Information from LSTM Models Using Cell State Analysis
Cloud Dynamics to Soil Moisture: AI-Enabled Shortcuts in Hydrologic Prediction
Flood Inundation and Suspended Sediment Flux Projections Indicate Significant Toxic Metal Inputs in the NE Gulf of Mexico as a Result of Hurricane Impacts
Leveraging Large-Scale Meteorological Geospatial Information to Predict Snow Water Equivalent with Machine Learning
Scoring Rule-Based LSTM Models for Flow Forecasting
SWAT Machine Learning-Integrated Modeling for Ranking Watershed Vulnerability to Climate Variability and Land-Use Change in Alabama, USA, in 1990–2023
Towards Representing Pluvial Flooding within NOAA’s NextGen Modeling Framework
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