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  "alternative_identifier": "10.2166/hydro.2024.268",
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  "alternative_identifier": "10.2166/hydro.2024.268",
            
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  "author": null,
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  "authors": [
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  "authors": [
            
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    {
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    {
            
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      "author_department": "Department of Plant and Soil Sciences",
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      "author_department": "Department of Plant and Soil Sciences",
            
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      "author_email": "ceschutte34@gmail.com",
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      "author_email": "ceschutte34@gmail.com",
            
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      "author_name": "Christiaan ",
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      "author_name": "Christiaan ",
            
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      "author_organization": "University of Pretoria",
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      "author_organization": "University of Pretoria",
            
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      "author_surname": "Schutte",
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      "author_surname": "Schutte",
            
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      "contact_same_as_author": "True"
            
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    },
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    {
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    {
            
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      "author_department": "Department of Plant and Soil Sciences",
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      "author_department": "Department of Plant and Soil Sciences",
            
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      "author_email": "vanderLaanM@arc.agric.za",
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      "author_email": "vanderLaanM@arc.agric.za",
            
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      "author_name": "Michael",
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      "author_name": "Michael",
            
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      "author_organization": "University of Pretoria",
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      "author_organization": "University of Pretoria",
            
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      "author_surname": "van der Laan"
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      "author_surname": "van der Laan"
            
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    },
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    },
            
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    {
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    {
            
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      "author_department": "Department of Geography, Geoinformatics 
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      "author_department": "Department of Geography, Geoinformatics 
            
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and Meteorology",
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and Meteorology",
            
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      "author_email": "barend.vandermerwe@up.ac.za",
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      "author_email": "barend.vandermerwe@up.ac.za",
            
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      "author_name": "Barend",
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      "author_name": "Barend",
            
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      "author_organization": "University of Pretoria",
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      "author_organization": "University of Pretoria",
            
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      "author_surname": "van der Merwe"
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      "author_surname": "van der Merwe"
            
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    }
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    }
            
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  ],
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  ],
            
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  "citation_title": "SCHUTTE C, VAN DER LAAN M and VAN DER MERWE B 
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  "citation_title": "SCHUTTE C, VAN DER LAAN M and VAN DER MERWE B 
            
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(2024) Leveraging historic streamflow and weather data with deep 
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(2024) Leveraging historic streamflow and weather data with deep 
            
33
learning for enhanced streamflow predictions. Journal of 
33
learning for enhanced streamflow predictions. Journal of 
            
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Hydroinformatics 26 (4) 835. http://doi.org/10.2166/hydro.2024.268.",
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Hydroinformatics 26 (4) 835. http://doi.org/10.2166/hydro.2024.268.",
            
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    {
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      "contact_department": "Department of Plant and Soil Sciences",
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      "contact_department": "Department of Plant and Soil Sciences",
            
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      "contact_email": "ceschutte34@gmail.com",
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      "contact_email": "ceschutte34@gmail.com",
            
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      "contact_name": "Christiaan ",
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      "contact_name": "Christiaan ",
            
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      "contact_orgnization": "University of Pretoria"
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      "contact_orgnization": "University of Pretoria"
            
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  "data_classification": "time series",
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  "data_classification": "time series",
            
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  "data_collection_organization": "Water Research Observatory",
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  "data_collection_organization": "Water Research Observatory",
            
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  "data_reference_date": [
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  "data_reference_date": [
            
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    {
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      "data_reference_date_from": "1979-10-01",
            
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  ],
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  "data_structure_category": "structured",
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  "data_structure_category": "structured",
            
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  "dataset_language": "English",
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  "dataset_language": "English",
            
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  "dataset_license_url": " 
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  "dataset_license_url": " 
            
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https://creativecommons.org/licenses/by-sa/4.0/",
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https://creativecommons.org/licenses/by-sa/4.0/",
            
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  "did_author_or_contact_organization_collect_the_data": "false",
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  "email": "vanderLaanM@arc.agric.za",
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  "email": "vanderLaanM@arc.agric.za",
            
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  "keywords": "GRU, LSTM, rainfall-runoff modelling, streamflow 
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  "keywords": "GRU, LSTM, rainfall-runoff modelling, streamflow 
            
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prediction, deep learning, Steelpoort River, Olifants basin, CHIRPS, 
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prediction, deep learning, Steelpoort River, Olifants basin, CHIRPS, 
            
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NASA POWER, ARC",
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NASA POWER, ARC",
            
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  "license": "Open (Creative Commons)",
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  "license": "Open (Creative Commons)",
            
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  "license_id": null,
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  "license_id": null,
            
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  "license_title": null,
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  "name": "deep-learning-for-streamflow-prediction-project-data",
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  "name": "deep-learning-for-streamflow-prediction-project-data",
            
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  "notes": "This study evaluates deep learning methods for daily 
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  "notes": "This study evaluates deep learning methods for daily 
            
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streamflow prediction in data-scarce contexts, using two headwater 
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streamflow prediction in data-scarce contexts, using two headwater 
            
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catchments of the Steelpoort River (Olifants River basin, South 
80
catchments of the Steelpoort River (Olifants River basin, South 
            
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Africa) as case studies. Gated Recurrent Unit (GRU) and Long 
81
Africa) as case studies. Gated Recurrent Unit (GRU) and Long 
            
82
Short-Term Memory (LSTM) networks are trained using daily rainfall and 
82
Short-Term Memory (LSTM) networks are trained using daily rainfall and 
            
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temperature data, together with historic streamflow in an 
83
temperature data, together with historic streamflow in an 
            
84
autoregressive manner. The approach incorporates predicted streamflow 
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autoregressive manner. The approach incorporates predicted streamflow 
            
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values into the look-back window to generate continuous predictions 
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values into the look-back window to generate continuous predictions 
            
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across the testing period, enabling both gap-filling and short-term 
86
across the testing period, enabling both gap-filling and short-term 
            
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forecasting. To ensure physically consistent outputs, the authors 
87
forecasting. To ensure physically consistent outputs, the authors 
            
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modify standard GRU/LSTM architectures by (i) changing the activation 
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modify standard GRU/LSTM architectures by (i) changing the activation 
            
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function in the final hidden layer and (ii) applying a non-negative 
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function in the final hidden layer and (ii) applying a non-negative 
            
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constraint in the dense layer to prevent negative streamflow 
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constraint in the dense layer to prevent negative streamflow 
            
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simulations. Weather inputs are sourced from (1) an ARC station record 
91
simulations. Weather inputs are sourced from (1) an ARC station record 
            
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and (2) gridded products (CHIRPS rainfall and NASA POWER/NASAP 
92
and (2) gridded products (CHIRPS rainfall and NASA POWER/NASAP 
            
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variables), combined with DWS streamflow data from gauging stations 
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variables), combined with DWS streamflow data from gauging stations 
            
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B4H007 and B4H001. Training spans 1 Oct 1979\u201330 Sep 1997 and 
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B4H007 and B4H001. Training spans 1 Oct 1979\u201330 Sep 1997 and 
            
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testing spans 1 Oct 1997\u201322 Feb 2002. Results show that models 
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testing spans 1 Oct 1997\u201322 Feb 2002. Results show that models 
            
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using ARC station data achieve reliable predictions, while models 
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using ARC station data achieve reliable predictions, while models 
            
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using gridded datasets provide moderately accurate predictions, 
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using gridded datasets provide moderately accurate predictions, 
            
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indicating potential for low-cost streamflow estimation where gauges 
98
indicating potential for low-cost streamflow estimation where gauges 
            
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are inactive or incomplete. The work contributes a practical method 
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are inactive or incomplete. The work contributes a practical method 
            
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for southern Africa where monitoring networks have declined.",
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for southern Africa where monitoring networks have declined.",
            
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