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

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

DISCLAIMER: 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.
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