High-resolution flow and phosphorus forecasting using ANN models, catering for extremes in the case of the River Swale (UK)

Timis, Elisabeta Cristina ORCID: https://orcid.org/0000-0002-9671-9014; Hangan, Horia; Cristea, Vasile Mircea; Mihaly, Norbert Botond ORCID: https://orcid.org/0000-0002-0126-2657; Hutchins, Michael George ORCID: https://orcid.org/0000-0003-3764-5331. 2025 High-resolution flow and phosphorus forecasting using ANN models, catering for extremes in the case of the River Swale (UK) [in special issue: Hydrodynamics and water quality of rivers and lakes] Hydrology, 12 (2), 20. 23, pp. 10.3390/hydrology12020020

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Official URL: https://doi.org/10.3390/hydrology12020020

Abstract/Summary

The forecasting of river flows and pollutant concentrations is essential in supporting mitigation measures for anthropogenic and climate change effects on rivers and their environment. This paper addresses two aspects receiving little attention in the literature: high-resolution (sub-daily) data-driven modeling and the prediction of phosphorus compounds. It presents a series of artificial neural networks (ANNs) to forecast flows and the concentrations of soluble reactive phosphorus (SRP) and total phosphorus (TP) under a wide range of conditions, including low flows and storm events (0.74 to 484 m3/s). Results show correct forecast along a stretch of the River Swale (UK) with an anticipation of up to 15 h, at resolutions of up to 3 h. The concentration prediction is improved compared to a previous application of an advection–dispersion model.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.3390/hydrology12020020
UKCEH and CEH Sections/Science Areas:	Environmental Pressures and Responses (2025-)
ISSN:	2306-5338
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	pollutant transport forecast, hydrological model, artificial neural networks, river flow forecast, in-river phosphorus model, high-resolution model
NORA Subject Terms:	Hydrology Data and Information
Related URLs:	Dataset
Date made live:	28 Jan 2025 09:37 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538813

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.3390/hydrology12020020

UKCEH and CEH Sections/Science Areas:

Environmental Pressures and Responses (2025-)

ISSN:

2306-5338

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

pollutant transport forecast, hydrological model, artificial neural networks, river flow forecast, in-river phosphorus model, high-resolution model