Breytingar
On 19. febrúar 2026 kl. 10:13:58 UTC,
Michael van der Laan:
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| 3 | "alternative_identifier": "10.2166/hydro.2024.268", | 3 | "alternative_identifier": "10.2166/hydro.2024.268", | ||
| 4 | "author": null, | 4 | "author": null, | ||
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| 6 | "authors": [ | 6 | "authors": [ | ||
| 7 | { | 7 | { | ||
| 8 | "author_department": "Department of Plant and Soil Sciences", | 8 | "author_department": "Department of Plant and Soil Sciences", | ||
| 9 | "author_email": "ceschutte34@gmail.com", | 9 | "author_email": "ceschutte34@gmail.com", | ||
| 10 | "author_name": "Christiaan ", | 10 | "author_name": "Christiaan ", | ||
| 11 | "author_organization": "University of Pretoria", | 11 | "author_organization": "University of Pretoria", | ||
| 12 | "author_surname": "Schutte", | 12 | "author_surname": "Schutte", | ||
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| 14 | }, | 14 | }, | ||
| 15 | { | 15 | { | ||
| 16 | "author_department": "Department of Plant and Soil Sciences", | 16 | "author_department": "Department of Plant and Soil Sciences", | ||
| 17 | "author_email": "vanderLaanM@arc.agric.za", | 17 | "author_email": "vanderLaanM@arc.agric.za", | ||
| 18 | "author_name": "Michael", | 18 | "author_name": "Michael", | ||
| 19 | "author_organization": "University of Pretoria", | 19 | "author_organization": "University of Pretoria", | ||
| 20 | "author_surname": "van der Laan" | 20 | "author_surname": "van der Laan" | ||
| 21 | }, | 21 | }, | ||
| 22 | { | 22 | { | ||
| 23 | "author_department": "Department of Geography, Geoinformatics | 23 | "author_department": "Department of Geography, Geoinformatics | ||
| 24 | and Meteorology", | 24 | and Meteorology", | ||
| 25 | "author_email": "barend.vandermerwe@up.ac.za", | 25 | "author_email": "barend.vandermerwe@up.ac.za", | ||
| 26 | "author_name": "Barend", | 26 | "author_name": "Barend", | ||
| 27 | "author_organization": "University of Pretoria", | 27 | "author_organization": "University of Pretoria", | ||
| 28 | "author_surname": "van der Merwe" | 28 | "author_surname": "van der Merwe" | ||
| 29 | } | 29 | } | ||
| 30 | ], | 30 | ], | ||
| 31 | "citation_title": "SCHUTTE C, VAN DER LAAN M and VAN DER MERWE B | 31 | "citation_title": "SCHUTTE C, VAN DER LAAN M and VAN DER MERWE B | ||
| 32 | (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.", | 34 | Hydroinformatics 26 (4) 835. http://doi.org/10.2166/hydro.2024.268.", | ||
| 35 | "contact_person": [ | 35 | "contact_person": [ | ||
| 36 | { | 36 | { | ||
| 37 | "contact_department": "Department of Plant and Soil Sciences", | 37 | "contact_department": "Department of Plant and Soil Sciences", | ||
| 38 | "contact_email": "ceschutte34@gmail.com", | 38 | "contact_email": "ceschutte34@gmail.com", | ||
| 39 | "contact_name": "Christiaan ", | 39 | "contact_name": "Christiaan ", | ||
| 40 | "contact_orgnization": "University of Pretoria" | 40 | "contact_orgnization": "University of Pretoria" | ||
| 41 | } | 41 | } | ||
| 42 | ], | 42 | ], | ||
| 43 | "creator_user_id": "fa7af5b8-32a7-4df0-90a7-c49fbefb1fbd", | 43 | "creator_user_id": "fa7af5b8-32a7-4df0-90a7-c49fbefb1fbd", | ||
| 44 | "data_classification": "time series", | 44 | "data_classification": "time series", | ||
| 45 | "data_collection_organization": "Water Research Observatory", | 45 | "data_collection_organization": "Water Research Observatory", | ||
| 46 | "data_reference_date": [ | 46 | "data_reference_date": [ | ||
| 47 | { | 47 | { | ||
| 48 | "data_reference_date_from": "1979-10-01", | 48 | "data_reference_date_from": "1979-10-01", | ||
| 49 | "data_reference_date_to": "2002-02-22" | 49 | "data_reference_date_to": "2002-02-22" | ||
| 50 | } | 50 | } | ||
| 51 | ], | 51 | ], | ||
| 52 | "data_structure_category": "structured", | 52 | "data_structure_category": "structured", | ||
| 53 | "dataset_language": "English", | 53 | "dataset_language": "English", | ||
| 54 | "dataset_license_url": " | 54 | "dataset_license_url": " | ||
| 55 | https://creativecommons.org/licenses/by-sa/4.0/", | 55 | https://creativecommons.org/licenses/by-sa/4.0/", | ||
| 56 | "did_author_or_contact_organization_collect_the_data": "false", | 56 | "did_author_or_contact_organization_collect_the_data": "false", | ||
| 57 | "email": "vanderLaanM@arc.agric.za", | 57 | "email": "vanderLaanM@arc.agric.za", | ||
| 58 | "groups": [], | 58 | "groups": [], | ||
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| 60 | "isopen": false, | 60 | "isopen": false, | ||
| 61 | "keywords": "GRU, LSTM, rainfall-runoff modelling, streamflow | 61 | "keywords": "GRU, LSTM, rainfall-runoff modelling, streamflow | ||
| 62 | prediction, deep learning, Steelpoort River, Olifants basin, CHIRPS, | 62 | prediction, deep learning, Steelpoort River, Olifants basin, CHIRPS, | ||
| 63 | NASA POWER, ARC", | 63 | NASA POWER, ARC", | ||
| 64 | "license": "Open (Creative Commons)", | 64 | "license": "Open (Creative Commons)", | ||
| 65 | "license_id": null, | 65 | "license_id": null, | ||
| 66 | "license_title": null, | 66 | "license_title": null, | ||
| 67 | "maintainer": null, | 67 | "maintainer": null, | ||
| 68 | "maintainer_email": null, | 68 | "maintainer_email": null, | ||
| 69 | "metadata_created": "2025-10-22T11:55:59.470668", | 69 | "metadata_created": "2025-10-22T11:55:59.470668", | ||
| t | 70 | "metadata_modified": "2026-02-19T10:13:40.893471", | t | 70 | "metadata_modified": "2026-02-19T10:13:57.996429", |
| 71 | "minimum_maximum_extent": [ | 71 | "minimum_maximum_extent": [ | ||
| 72 | { | 72 | { | ||
| 73 | "maximum_vertical_extent": "2263", | 73 | "maximum_vertical_extent": "2263", | ||
| 74 | "minimum_vertical_extent": "1336" | 74 | "minimum_vertical_extent": "1336" | ||
| 75 | } | 75 | } | ||
| 76 | ], | 76 | ], | ||
| 77 | "name": "deep-learning-for-streamflow-prediction-project-data", | 77 | "name": "deep-learning-for-streamflow-prediction-project-data", | ||
| 78 | "notes": "This study evaluates deep learning methods for daily | 78 | "notes": "This study evaluates deep learning methods for daily | ||
| 79 | streamflow prediction in data-scarce contexts, using two headwater | 79 | streamflow prediction in data-scarce contexts, using two headwater | ||
| 80 | catchments of the Steelpoort River (Olifants River basin, South | 80 | catchments of the Steelpoort River (Olifants River basin, South | ||
| 81 | 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 | ||
| 85 | values into the look-back window to generate continuous predictions | 85 | values into the look-back window to generate continuous predictions | ||
| 86 | across the testing period, enabling both gap-filling and short-term | 86 | across the testing period, enabling both gap-filling and short-term | ||
| 87 | 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 | ||
| 89 | function in the final hidden layer and (ii) applying a non-negative | 89 | function in the final hidden layer and (ii) applying a non-negative | ||
| 90 | constraint in the dense layer to prevent negative streamflow | 90 | constraint in the dense layer to prevent negative streamflow | ||
| 91 | 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 | ||
| 93 | variables), combined with DWS streamflow data from gauging stations | 93 | variables), combined with DWS streamflow data from gauging stations | ||
| 94 | B4H007 and B4H001. Training spans 1 Oct 1979\u201330 Sep 1997 and | 94 | B4H007 and B4H001. Training spans 1 Oct 1979\u201330 Sep 1997 and | ||
| 95 | 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 | ||
| 96 | using ARC station data achieve reliable predictions, while models | 96 | using ARC station data achieve reliable predictions, while models | ||
| 97 | using gridded datasets provide moderately accurate predictions, | 97 | using gridded datasets provide moderately accurate predictions, | ||
| 98 | indicating potential for low-cost streamflow estimation where gauges | 98 | indicating potential for low-cost streamflow estimation where gauges | ||
| 99 | are inactive or incomplete. The work contributes a practical method | 99 | are inactive or incomplete. The work contributes a practical method | ||
| 100 | for southern Africa where monitoring networks have declined.", | 100 | for southern Africa where monitoring networks have declined.", | ||
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| 103 | "organization": { | 103 | "organization": { | ||
| 104 | "approval_status": "approved", | 104 | "approval_status": "approved", | ||
| 105 | "created": "2025-10-13T09:33:45.492986", | 105 | "created": "2025-10-13T09:33:45.492986", | ||
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| 117 | "publication_date": "2024-02-28", | 117 | "publication_date": "2024-02-28", | ||
| 118 | "publisher": "Journal of Hydroinformatics", | 118 | "publisher": "Journal of Hydroinformatics", | ||
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| 224 | "title": "Leveraging historic streamflow and weather data with deep | 224 | "title": "Leveraging historic streamflow and weather data with deep | ||
| 225 | learning for enhanced streamflow predictions", | 225 | learning for enhanced streamflow predictions", | ||
| 226 | "type": "dataset", | 226 | "type": "dataset", | ||
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