Streamflow prediction using artificial neural networks and soil moisture proxies
Machine learning models have been used extensively in hydrology, but issues persist with regard to their transparency, and there is currently no identifiable best practice for forcing variables in streamflow or flood modeling. In this paper, using data from the Centre for Ecology & Hydrology’s N...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Environmental Data Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460224000487/type/journal_article |
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| _version_ | 1841526400201261056 |
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| author | Robert Edwin Rouse Doran Khamis Scott Hosking Allan McRobie Emily Shuckburgh |
| author_facet | Robert Edwin Rouse Doran Khamis Scott Hosking Allan McRobie Emily Shuckburgh |
| author_sort | Robert Edwin Rouse |
| collection | DOAJ |
| description | Machine learning models have been used extensively in hydrology, but issues persist with regard to their transparency, and there is currently no identifiable best practice for forcing variables in streamflow or flood modeling. In this paper, using data from the Centre for Ecology & Hydrology’s National River Flow Archive and from the European Centre for Medium-Range Weather Forecasts, we present a study that focuses on the input variable set for a neural network streamflow model to demonstrate how certain variables can be internalized, leading to a compressed feature set. By highlighting this capability to learn effectively using proxy variables, we demonstrate a more transferable framework that minimizes sensing requirements and that enables a route toward generalizing models. |
| format | Article |
| id | doaj-art-137b9213eb154270beef398e3923256f |
| institution | Kabale University |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-137b9213eb154270beef398e3923256f2025-01-16T21:49:47ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2024.48Streamflow prediction using artificial neural networks and soil moisture proxiesRobert Edwin Rouse0https://orcid.org/0009-0000-4601-0210Doran Khamis1Scott Hosking2Allan McRobie3Emily Shuckburgh4Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UKUK Centre for Ecology & Hydrology, Wallingford OX10 8BB, UKBritish Antarctic Survey, Cambridge CB3 0ET, UK The Alan Turing Institute, London NW1 2DB, UKDepartment of Engineering, University of Cambridge, Cambridge CB2 1PZ, UKDepartment of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, UKMachine learning models have been used extensively in hydrology, but issues persist with regard to their transparency, and there is currently no identifiable best practice for forcing variables in streamflow or flood modeling. In this paper, using data from the Centre for Ecology & Hydrology’s National River Flow Archive and from the European Centre for Medium-Range Weather Forecasts, we present a study that focuses on the input variable set for a neural network streamflow model to demonstrate how certain variables can be internalized, leading to a compressed feature set. By highlighting this capability to learn effectively using proxy variables, we demonstrate a more transferable framework that minimizes sensing requirements and that enables a route toward generalizing models.https://www.cambridge.org/core/product/identifier/S2634460224000487/type/journal_articleartificial neural networkshydrologymachine learningstreamflow |
| spellingShingle | Robert Edwin Rouse Doran Khamis Scott Hosking Allan McRobie Emily Shuckburgh Streamflow prediction using artificial neural networks and soil moisture proxies Environmental Data Science artificial neural networks hydrology machine learning streamflow |
| title | Streamflow prediction using artificial neural networks and soil moisture proxies |
| title_full | Streamflow prediction using artificial neural networks and soil moisture proxies |
| title_fullStr | Streamflow prediction using artificial neural networks and soil moisture proxies |
| title_full_unstemmed | Streamflow prediction using artificial neural networks and soil moisture proxies |
| title_short | Streamflow prediction using artificial neural networks and soil moisture proxies |
| title_sort | streamflow prediction using artificial neural networks and soil moisture proxies |
| topic | artificial neural networks hydrology machine learning streamflow |
| url | https://www.cambridge.org/core/product/identifier/S2634460224000487/type/journal_article |
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