Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions

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Authors

Schutte, Christiaan E.
Van der Laan, Michael
Van der Merwe, Barend Jacobus

Journal Title

Journal ISSN

Volume Title

Publisher

IWA Publishing

Abstract

Streamflow information is crucial for effectively managing water resources. The declining number of active gauging stations in many rivers is a global concern, necessitating the need for reliable streamflow estimates. Deep learning techniques offer potential solutions, but their application in southern Africa remains largely underexplored. To fill this gap, this study evaluated the predictive performance of gated recurrent unit (GRU) and long short-term memory (LSTM) networks using two headwater catchments of the Steelpoort River, South Africa, as case studies. The model inputs included rainfall, maximum, and minimum temperature, as well as past streamflow, which was utilized in an autoregressive sense. The inclusion of streamflow in this way allowed for the incorporation of simulated streamflow values into the look-back window for predicting the streamflow of the testing set. Two modifications were required to the GRU and LSTM architectures to ensure physically consistent predictions, including a change in the activation function of the GRU/LSTM cells in the final hidden layer, and a non-negative constraint that was used in the dense layer. Models trained using commercial weather station data produced reliable streamflow estimates, while moderately accurate predictions were obtained using freely available gridded weather data.

Description

DATA AVAILABILITY STATEMENT : All relevant data are available from an online repository or repositories:https://data.waterresearchobservatory.org/metadataform/ deep-learning-for-streamflow-prediction-project-data.

Keywords

Rainfall-runoff modelling, Gated recurrent unit (GRU), Long short-term memory (LSTM), Streamflow information, Water resources, SDG-06: Clean water and sanitation, SDG-13: Climate action

Sustainable Development Goals

SDG-06:Clean water and sanitation
SDG-13:Climate action

Citation

Schutte, C., Van der Laan, M. & Van der Merwe, B. 2024, 'Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions', Journal of Hydroinformatics, vol. 26, no. 4, pp. 825-852. DOI: 10.2166/hydro.2024.268