Articles | Volume 13, issue 9
Geosci. Model Dev., 13, 4253–4270, 2020
https://doi.org/10.5194/gmd-13-4253-2020
Geosci. Model Dev., 13, 4253–4270, 2020
https://doi.org/10.5194/gmd-13-4253-2020

Model evaluation paper 15 Sep 2020

Model evaluation paper | 15 Sep 2020

ML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrients

Benya Wang et al.

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Cited articles

Adams, R., Arafat, Y., Eate, V., Grace, M. R., Saffarpour, S., Weatherley, A. J., and Western, A. W.: A catchment study of sources and sinks of nutrients and sediments in south-east Australia, J. Hydrol., 515, 166–179, https://doi.org/10.1016/j.jhydrol.2014.04.034, 2014. 
Álvarez-Cabria, M., Barquín, J., and Peñas, F. J.: Modelling the spatial and seasonal variability of water quality for entire river networks: Relationships with natural and anthropogenic factors, Sci. Total Environ., 545–546, 152–162, https://doi.org/10.1016/j.scitotenv.2015.12.109, 2016. 
Barron, O., Donn, M., Furby, S., Chia, J., and Johnstone, C.: Groundwater contribution to nutrient export from the Ellen Brook catchment, available at: http://www.clw.csiro.au/publications/waterforahealthycountry/2009/wfhc-groundwater-Ellen-Brook-catchment.pdf (last access: 9 September 2020), 2009. 
Belgiu, M. and Drăgu, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm. Remote Sens., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016. 
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Short summary
Surface water nutrients are essential to manage water quality, but it is hard to analyse trends. We developed a hybrid model and compared with other models for the prediction of six different nutrients. Our results showed that the hybrid model had significantly higher accuracy and lower prediction uncertainty for almost all nutrient species. The hybrid model provides a flexible method to combine data of varied resolution and quality and is accurate for the prediction of nutrient concentrations.