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Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework

Nitrogen (N) and Phosphorus (P) are essential nutritional elements for life processes in water bodies. However, in excessive quantities, they may represent a significant source of aquatic pollution. Eutrophication has become a widespread issue rising from a chemical nutrient imbalance and is largely...

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Autores principales: Shen, Longzhu Q., Amatulli, Giuseppe, Sethi, Tushar, Raymond, Peter, Domisch, Sami
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256043/
https://www.ncbi.nlm.nih.gov/pubmed/32467642
http://dx.doi.org/10.1038/s41597-020-0478-7
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author Shen, Longzhu Q.
Amatulli, Giuseppe
Sethi, Tushar
Raymond, Peter
Domisch, Sami
author_facet Shen, Longzhu Q.
Amatulli, Giuseppe
Sethi, Tushar
Raymond, Peter
Domisch, Sami
author_sort Shen, Longzhu Q.
collection PubMed
description Nitrogen (N) and Phosphorus (P) are essential nutritional elements for life processes in water bodies. However, in excessive quantities, they may represent a significant source of aquatic pollution. Eutrophication has become a widespread issue rising from a chemical nutrient imbalance and is largely attributed to anthropogenic activities. In view of this phenomenon, we present a new geo-dataset to estimate and map the concentrations of N and P in their various chemical forms at a spatial resolution of 30 arc-second (∼1 km) for the conterminous US. The models were built using Random Forest (RF), a machine learning algorithm that regressed the seasonally measured N and P concentrations collected at 62,495 stations across the US streams for the period of 1994–2018 onto a set of 47 in-house built environmental variables that are available at a near-global extent. The seasonal models were validated through internal and external validation procedures and the predictive powers measured by Pearson Coefficients reached approximately 0.66 on average.
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spelling pubmed-72560432020-06-10 Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework Shen, Longzhu Q. Amatulli, Giuseppe Sethi, Tushar Raymond, Peter Domisch, Sami Sci Data Data Descriptor Nitrogen (N) and Phosphorus (P) are essential nutritional elements for life processes in water bodies. However, in excessive quantities, they may represent a significant source of aquatic pollution. Eutrophication has become a widespread issue rising from a chemical nutrient imbalance and is largely attributed to anthropogenic activities. In view of this phenomenon, we present a new geo-dataset to estimate and map the concentrations of N and P in their various chemical forms at a spatial resolution of 30 arc-second (∼1 km) for the conterminous US. The models were built using Random Forest (RF), a machine learning algorithm that regressed the seasonally measured N and P concentrations collected at 62,495 stations across the US streams for the period of 1994–2018 onto a set of 47 in-house built environmental variables that are available at a near-global extent. The seasonal models were validated through internal and external validation procedures and the predictive powers measured by Pearson Coefficients reached approximately 0.66 on average. Nature Publishing Group UK 2020-05-28 /pmc/articles/PMC7256043/ /pubmed/32467642 http://dx.doi.org/10.1038/s41597-020-0478-7 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Shen, Longzhu Q.
Amatulli, Giuseppe
Sethi, Tushar
Raymond, Peter
Domisch, Sami
Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework
title Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework
title_full Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework
title_fullStr Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework
title_full_unstemmed Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework
title_short Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework
title_sort estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256043/
https://www.ncbi.nlm.nih.gov/pubmed/32467642
http://dx.doi.org/10.1038/s41597-020-0478-7
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