Category: paper

Posts

19 October 2022 / / paper / code

In this paper, we present the results of the “Rate My Hydrograph” study, where we compare expert ratings of simulated hydrographs with quantitative metrics.

05 August 2022 / / paper
08 July 2022 / / paper / code
20 June 2022 / / paper / code
16 June 2022 / / paper / code / dataset
20 January 2022 / / paper / code

This paper investigates the hypothesis that the lack of enforced mass conservation is the main reason that deep learning models outperform traditional hydrology models.

15 November 2021 / / paper / code
25 October 2021 / / paper / code
20 May 2021 / / paper / code

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.

14 April 2021 / / paper / code
14 January 2021 / / paper / code

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.

13 November 2020 / / paper
05 June 2020 / / paper / code
23 November 2019 / / paper / code

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.

17 November 2019 / / paper / code

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

02 August 2019 / / paper / code

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).