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  "agreement": "true",
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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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  "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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      "contact_same_as_author": "True"
            
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    },
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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 
32
(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 
            
34
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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  "contact_person": [
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  "contact_person": [
            
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    {
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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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    }
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    }
            
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  ],
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  "creator_user_id": "fa7af5b8-32a7-4df0-90a7-c49fbefb1fbd",
            
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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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    {
            
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      "data_reference_date_from": "1979-10-01",
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      "data_reference_date_from": "1979-10-01",
            
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      "data_reference_date_to": "2002-02-22"
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      "data_reference_date_to": "2002-02-22"
            
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    }
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    }
            
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  ],
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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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  "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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  "groups": [],
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  "isopen": false,
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  "isopen": false,
            
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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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  "license_title": null,
            
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  "metadata_modified": "2026-04-22T20:03:37.828207",
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  "metadata_modified": "2026-05-07T13:34:21.881048",
            
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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 
79
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 
            
83
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 
84
autoregressive manner. The approach incorporates predicted streamflow 
            
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values into the look-back window to generate continuous predictions 
85
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 
            
88
modify standard GRU/LSTM architectures by (i) changing the activation 
88
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 
90
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 
            
92
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 
95
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 
99
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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