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Found 7 papers, 2 papers with code
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift.
Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy
Without using an ensemble or post-processor, {\delta} models can obtain a median Nash Sutcliffe efficiency of 0. 732 for 671 basins across the USA for the Daymet forcing dataset, compared to 0. 748 from a state-of-the-art LSTM model with the same setup.
A Robust Statistical Analysis of the Role of Hydropower on the System Electricity Price and Price Volatility
Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage) for providing energy in times of high demand on the grid.
Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy
However, dammed basins must be present in the training dataset.
The data synergy effects of time-series deep learning models in hydrology
We hypothesize that DL models automatically adjust their internal representations to identify commonalities while also providing sufficient discriminatory information to the model.
Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modeling
While long short-term memory (LSTM) models have demonstrated stellar performance with streamflow predictions, there are major risks in applying these models in contiguous regions with no gauges, or predictions in ungauged regions (PUR) problems.
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
The behaviors and skills of models in many geosciences (e. g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration.
