River reach-level machine learning estimation of nutrient concentrations in Great Britain
Nitrogen (N) and phosphorus (P) are essential nutrients necessary for plant growth and support life in aquatic ecosystems. However, excessive N and P can lead to algal blooms that deplete oxygen and lead to fish death and the release of toxins that are harmful to humans. Estimates of N and P levels...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Water |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frwa.2023.1244024/full |
| _version_ | 1797679149949648896 |
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| author | Chak-Hau Michael Tso Chak-Hau Michael Tso Eugene Magee Eugene Magee David Huxley Michael Eastman Michael Eastman Matthew Fry Matthew Fry |
| author_facet | Chak-Hau Michael Tso Chak-Hau Michael Tso Eugene Magee Eugene Magee David Huxley Michael Eastman Michael Eastman Matthew Fry Matthew Fry |
| author_sort | Chak-Hau Michael Tso |
| collection | DOAJ |
| description | Nitrogen (N) and phosphorus (P) are essential nutrients necessary for plant growth and support life in aquatic ecosystems. However, excessive N and P can lead to algal blooms that deplete oxygen and lead to fish death and the release of toxins that are harmful to humans. Estimates of N and P levels in rivers are typically calculated at station or grid (>1 km) scale; therefore, it is difficult to visualise the evolution of water quality as water travels downstream. Using a high-resolution reach-scale river network and associating each reach with land cover fractions and catchment descriptors, we trained random forest models on aggregated data (2010–2020) from the Environmental Agency Open Water Quality Data Archive for 2,343 stations to predict long-term nitrate and orthophosphate concentrations at each river reach in Great Britain (GB). We separated the model training and predictions for different seasons to investigate the potential difference in feature importance. Our model predicted concentrations with an average testing coefficient of determination (R2) of 0.71 for nitrate and 0.58 for orthophosphate using 5-fold cross-validation. Our model showed slightly better performance for higher Strahler stream orders, highlighting the challenges of making predictions in small streams. Our results revealed that arable and horticultural land use is the strongest and most reliable predictor for nitrate, while floodplain extents and standard percentage runoff are stronger predictors for orthophosphate. Nationally, higher orthophosphate concentrations were observed in urbanised areas. This study shows how combining a river network model with machine learning can easily provide a river network understanding of the spatial distribution of water quality levels. |
| first_indexed | 2024-03-11T23:10:19Z |
| format | Article |
| id | doaj.art-45d4e492919c48d982691a3278c86369 |
| institution | Directory Open Access Journal |
| issn | 2624-9375 |
| language | English |
| last_indexed | 2024-03-11T23:10:19Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Water |
| spelling | doaj.art-45d4e492919c48d982691a3278c863692023-09-21T09:19:37ZengFrontiers Media S.A.Frontiers in Water2624-93752023-09-01510.3389/frwa.2023.12440241244024River reach-level machine learning estimation of nutrient concentrations in Great BritainChak-Hau Michael Tso0Chak-Hau Michael Tso1Eugene Magee2Eugene Magee3David Huxley4Michael Eastman5Michael Eastman6Matthew Fry7Matthew Fry8UK Centre for Ecology and Hydrology, Lancaster, United KingdomCentre of Excellence for Environmental Data Science, Lancaster, United KingdomUK Centre for Ecology and Hydrology, Wallingford, United KingdomFormerly Data Science MSc Programme, School of Computing and Communications, Lancaster University, Lancaster, United KingdomFormerly Data Science MSc Programme, School of Computing and Communications, Lancaster University, Lancaster, United KingdomUK Centre for Ecology and Hydrology, Wallingford, United KingdomMet Office, Exeter, United KingdomCentre of Excellence for Environmental Data Science, Lancaster, United KingdomUK Centre for Ecology and Hydrology, Wallingford, United KingdomNitrogen (N) and phosphorus (P) are essential nutrients necessary for plant growth and support life in aquatic ecosystems. However, excessive N and P can lead to algal blooms that deplete oxygen and lead to fish death and the release of toxins that are harmful to humans. Estimates of N and P levels in rivers are typically calculated at station or grid (>1 km) scale; therefore, it is difficult to visualise the evolution of water quality as water travels downstream. Using a high-resolution reach-scale river network and associating each reach with land cover fractions and catchment descriptors, we trained random forest models on aggregated data (2010–2020) from the Environmental Agency Open Water Quality Data Archive for 2,343 stations to predict long-term nitrate and orthophosphate concentrations at each river reach in Great Britain (GB). We separated the model training and predictions for different seasons to investigate the potential difference in feature importance. Our model predicted concentrations with an average testing coefficient of determination (R2) of 0.71 for nitrate and 0.58 for orthophosphate using 5-fold cross-validation. Our model showed slightly better performance for higher Strahler stream orders, highlighting the challenges of making predictions in small streams. Our results revealed that arable and horticultural land use is the strongest and most reliable predictor for nitrate, while floodplain extents and standard percentage runoff are stronger predictors for orthophosphate. Nationally, higher orthophosphate concentrations were observed in urbanised areas. This study shows how combining a river network model with machine learning can easily provide a river network understanding of the spatial distribution of water quality levels.https://www.frontiersin.org/articles/10.3389/frwa.2023.1244024/fullriver networkmachine learningnutrientswater qualityrandom forest |
| spellingShingle | Chak-Hau Michael Tso Chak-Hau Michael Tso Eugene Magee Eugene Magee David Huxley Michael Eastman Michael Eastman Matthew Fry Matthew Fry River reach-level machine learning estimation of nutrient concentrations in Great Britain Frontiers in Water river network machine learning nutrients water quality random forest |
| title | River reach-level machine learning estimation of nutrient concentrations in Great Britain |
| title_full | River reach-level machine learning estimation of nutrient concentrations in Great Britain |
| title_fullStr | River reach-level machine learning estimation of nutrient concentrations in Great Britain |
| title_full_unstemmed | River reach-level machine learning estimation of nutrient concentrations in Great Britain |
| title_short | River reach-level machine learning estimation of nutrient concentrations in Great Britain |
| title_sort | river reach level machine learning estimation of nutrient concentrations in great britain |
| topic | river network machine learning nutrients water quality random forest |
| url | https://www.frontiersin.org/articles/10.3389/frwa.2023.1244024/full |
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