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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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This application 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

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 application is available under the terms of the Open Government Licence

Cite this application 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

Dr. Michael Tso
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 , Pollution , Python , random forest model , river , River network , UK-SCAPE , Water quality , web application
Last updated
27 February 2024 16:27