In this paper, we present the results of the “Rate My Hydrograph” study, where we compare expert ratings of simulated hydrographs with quantitative metrics.
Category: code
Posts
This paper performs a rigorous benchmark of traditional hydrologic models and an LSTM-based model for rainfall-runoff modeling.
This paper introduces the Caravan dataset, a global large-sample hydrology dataset that builds on cloud computing to be extensible by anyone.
Accompanying paper to our open source Python library NeuralHydrology.
This paper investigates the hypothesis that the lack of enforced mass conservation is the main reason that deep learning models outperform traditional hydrology models.
In this paper, we investigate the potential of using the LSTM as a post-processor for the US National Water Model.
Technical note that compares autoregression to data assimilation for deep learning models and rainfall-runoff modeling.
This paper investigates the hypothesis that deep learning models may not be reliable in extrapolating extreme events.
In this paper we show the benefits of using multiple meteorological forcing products at the same time in a single LSTM-based rainfall-runoff model over just using a single product.
Deep learning based uncertainty estimation techniques and benchmarking procedure for rainfall-runoff modeling.
In this study, we present a mass-conserving variant of the LSTM and its application to arithmetic tasks, traffic forecasting, modeling a pendulum and rainfall-runoff modeling.
In this manuscript we test LSTM-based rainfall-runoff models on the task of prediction in ungauged basins and show, that a single LSTM-based model does better prediction in ungauged basins than a traditional hydrological model that was specifically calibrated for each basin individually.
This paper investigates the influence of the number of training basins and the training period length on the model performance for the EA-LSTM and XGBoost
In this manuscript we show for the first time how to train a single LSTM-based neural network as general hydrology model for hundreds of basins. Furthermore, we proposed the Entity-Aware LSTM (EA-LSTM) in which static features are used explicitly to subset the model for a specific entity (here a catchment).