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Konstantinos Andreadis
University of Massachusetts AmherstMeeting roles in:
Combining climate reanalysis and remote sensing data to predict river discharge using a generalizable, distributed, deep learning model.
Do Neural Networks Dream of Electric Hydrographs? Reproducibility and Robustness in Hydrologic Machine Learning
Forecasting and Beyond: LSTM and Stochastic Approaches for Streamflow Simulation
Revealing Groundwater Connectivity and Flux Dynamics in the Laurentian Great Lakes Using SWOT Mission Observations and Multi-Source Data Integration
Advancing flood mapping and forecasting in New Zealand with GNSS-R airborne observations from Rongowai
Three years of the Rongowai GNSS-R airborne mission: The data archive and products
An Assessment of the Value of SWOT River and Lake Data for Hydrologic Modeling Using Physics-embedded Learning
Improving Streamflow Predictions in Reservoir-Influenced Basins Using Machine Learning
Evaluating LSTM Model Adaptability to Input Perturbations
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