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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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    {
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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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    {
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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 
            
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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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  "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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  "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_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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  "email": "vanderLaanM@arc.agric.za",
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  "email": "vanderLaanM@arc.agric.za",
            
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  "extras": [
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    {
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    {
            
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      "key": "data_reference_date_from",
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      "key": "data_reference_date_from",
            
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      "value": "1979-10-01"
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      "value": "1979-10-01"
            
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    {
            
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      "key": "data_reference_date_to",
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      "key": "data_reference_date_to",
            
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      "value": "2002-02-22"
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      "value": "2002-02-22"
            
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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_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 
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catchments of the Steelpoort River (Olifants River basin, South 
            
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Africa) as case studies. Gated Recurrent Unit (GRU) and Long 
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Africa) as case studies. Gated Recurrent Unit (GRU) and Long 
            
86
Short-Term Memory (LSTM) networks are trained using daily rainfall and 
86
Short-Term Memory (LSTM) networks are trained using daily rainfall and 
            
87
temperature data, together with historic streamflow in an 
87
temperature data, together with historic streamflow in an 
            
88
autoregressive manner. The approach incorporates predicted streamflow 
88
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 
90
across the testing period, enabling both gap-filling and short-term 
            
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forecasting. To ensure physically consistent outputs, the authors 
91
forecasting. To ensure physically consistent outputs, the authors 
            
92
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 
94
constraint in the dense layer to prevent negative streamflow 
            
95
simulations. Weather inputs are sourced from (1) an ARC station record 
95
simulations. Weather inputs are sourced from (1) an ARC station record 
            
96
and (2) gridded products (CHIRPS rainfall and NASA POWER/NASAP 
96
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 
99
testing spans 1 Oct 1997\u201322 Feb 2002. Results show that models 
            
100
using ARC station data achieve reliable predictions, while models 
100
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 
102
indicating potential for low-cost streamflow estimation where gauges 
            
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are inactive or incomplete. The work contributes a practical method 
103
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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