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Magee, E.; Huxley, D.; Tso, C.-H.M.

Random forest model to predict long-term seasonal nitrate and orthophosphate concentrations in British river reaches

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Publication of this model code by the EIDC does not signify any endorsement or approval. By accessing and using the resource, you acknowledge that it is entirely at your own risk and you are solely responsible for any loss or liability that may arise

Uses Environment Agency water quality data from the Water Quality Archive

This model code is available under the terms of the Open Government Licence

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https://doi.org/10.5285/ba208b6c-6f1a-43b1-867d-bc1adaff6445
This resource comprises two Jupyter notebooks that includes the model code in python to train a random forest model to predict long-term seasonal nitrate and orthophosphate concentrations at each river reach in Great Britain. The input features considered are catchment descriptors and land cover matched to the reaches. The training data is obtained from the Environmental Agency Water Quality Archive, 2010-2020. This method provides an effective way to map water quality data from stations to the river network.

A live demo of a web application to visualize the dataset can be viewed at https://moisture-wqmlviewer.datalabs.ceh.ac.uk/wqml_viewer
Publication date: 2023-09-05
60 downloads *
867 views *

More information

View numbers valid from 05 September 2023 Download numbers valid from 20 June 2024 (information prior to this was not collected)

Formats

Jupyter notebooks, Comma-separated values (CSV)

Spatial information

Study area
Spatial representation type
Tabular (text)
Spatial reference system
OSGB 1936 / British National Grid

Temporal information

Temporal extent
2010-01-01    to    2020-12-31

Provenance & quality

The machine learning model is trained on the following datasets:
* Land cover map
* NRFA Catchment descriptors
* UKCEH digital river network of Great Britain (1:50,000)
* Environmental Agency water quality archive
More information is provided in the supporting documentation accompanying this resource.

Please note that only a subset of the data (HA 72 Wyre and Lune) is provided for demonstration. This is because the underlying river network is separately licensed (https://catalogue.ceh.ac.uk/documents/7d5e42b6-7729-46c8-99e9-f9e4efddde1d).

Licensing and constraints

This model code is available under the terms of the Open Government Licence

Cite this model code as:
Magee, E.; Huxley, D.; Tso, C.-H.M. (2023). Random forest model to predict long-term seasonal nitrate and orthophosphate concentrations in British river reaches. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/ba208b6c-6f1a-43b1-867d-bc1adaff6445

BibTeX RIS

Uses Environment Agency water quality data from the Water Quality Archive

Correspondence/contact details

Tso, M.
UK Centre for Ecology & Hydrology
Lancaster Environment Centre, Library Avenue, Bailrigg
Lancaster
Lancashire
LA1 4AP
UNITED KINGDOM
 enquiries@ceh.ac.uk

Authors

Magee, E.
UK Centre for Ecology & Hydrology
Huxley, D.
University of Manchester
Tso, C.-H.M.
UK Centre for Ecology & Hydrology
ORCID iD icon https://orcid.org/0000-0002-2415-0826

Other contacts

Rights holder
UK Centre for Ecology & Hydrology
Custodian
NERC EDS Environmental Information Data Centre
 info@eidc.ac.uk
Publisher
NERC EDS Environmental Information Data Centre
 info@eidc.ac.uk

Additional metadata

Topic categories
environment
inlandWaters
Keywords
Hydrology , Jupyter notebook , Machine learning , modelling , Modelling , nitrate , orthophosphate , Python , random forest model , river , River network , UK-SCAPE , Water quality , web application
Last updated
21 March 2025 13:22