Streamflow prediction using artificial neural networks and soil moisture proxies

Rouse, Robert Edwin ORCID: https://orcid.org/0009-0000-4601-0210; Khamis, Doran; Hosking, Scott; McRobie, Allan; Shuckburgh, Emily. 2025 Streamflow prediction using artificial neural networks and soil moisture proxies. Environmental Data Science, 4, e5. 15, pp. 10.1017/eds.2024.48

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Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1017/eds.2024.48
UKCEH and CEH Sections/Science Areas:	Water Resources (Science Area 2017-24)
ISSN:	2634-4602
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	artificial neural networks, hydrology, machine learning, streamflow
NORA Subject Terms:	Hydrology Computer Science Data and Information
Related URLs:	Dataset Dataset Other
Date made live:	27 Jan 2025 14:23 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538808

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