Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse

Rapid industrialization and population growth have elevated the concerns over water quality. Excessive nitrates and phosphates in the water system have an adverse effect on the aquatic ecosystem. In recent years, machine learning (ML) algorithms have been extensively employed to estimate water quali...

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Autores principales:	Aayush Bhattarai, Sandeep Dhakal, Yogesh Gautam, Rabin Bhattarai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	nitrate concentration phosphorus concentration machine learning Bayesian optimization water pollution Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/fe5f98314c92407590b8d9f81e95a1a4
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spelling	oai:doaj.org-article:fe5f98314c92407590b8d9f81e95a1a42021-11-11T19:57:10ZPrediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse10.3390/w132130962073-4441https://doaj.org/article/fe5f98314c92407590b8d9f81e95a1a42021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/21/3096https://doaj.org/toc/2073-4441Rapid industrialization and population growth have elevated the concerns over water quality. Excessive nitrates and phosphates in the water system have an adverse effect on the aquatic ecosystem. In recent years, machine learning (ML) algorithms have been extensively employed to estimate water quality over traditional methods. In this study, the performance of nine different ML algorithms is evaluated to predict nitrate and phosphorus concentration for five different watersheds with different land-use practices. The land-use distribution affects the model performance for all methods. In urban watersheds, the regular and predictable nature of nitrate concentration from wastewater treatment plants results in more accurate estimates. For the nitrate prediction, ANN outperforms other ML models for the urban and agricultural watersheds, while RT-BO performs well for the forested Grand watershed. For the total phosphorus prediction, ensemble-BO and M-SVM outperform other ML models for the agricultural and forested watershed, while the ANN performs better than other ML models for the urban Cuyahoga watershed. In predicting phosphorus concentration, the model predictability is better for agricultural and forested watersheds. Regarding consistency, Bayesian optimized RT, ensemble, and GPR consistently yielded good performance for all watersheds. The methodology and results outlined in this study will assist policymakers in accurately predicting nitrate and phosphorus concentration which will be instrumental in drafting a proper plan to deal with the problem of water pollution.Aayush BhattaraiSandeep DhakalYogesh GautamRabin BhattaraiMDPI AGarticlenitrate concentrationphosphorus concentrationmachine learningBayesian optimizationwater pollutionHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3096, p 3096 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	nitrate concentration phosphorus concentration machine learning Bayesian optimization water pollution Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	nitrate concentration phosphorus concentration machine learning Bayesian optimization water pollution Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Aayush Bhattarai Sandeep Dhakal Yogesh Gautam Rabin Bhattarai Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse
description	Rapid industrialization and population growth have elevated the concerns over water quality. Excessive nitrates and phosphates in the water system have an adverse effect on the aquatic ecosystem. In recent years, machine learning (ML) algorithms have been extensively employed to estimate water quality over traditional methods. In this study, the performance of nine different ML algorithms is evaluated to predict nitrate and phosphorus concentration for five different watersheds with different land-use practices. The land-use distribution affects the model performance for all methods. In urban watersheds, the regular and predictable nature of nitrate concentration from wastewater treatment plants results in more accurate estimates. For the nitrate prediction, ANN outperforms other ML models for the urban and agricultural watersheds, while RT-BO performs well for the forested Grand watershed. For the total phosphorus prediction, ensemble-BO and M-SVM outperform other ML models for the agricultural and forested watershed, while the ANN performs better than other ML models for the urban Cuyahoga watershed. In predicting phosphorus concentration, the model predictability is better for agricultural and forested watersheds. Regarding consistency, Bayesian optimized RT, ensemble, and GPR consistently yielded good performance for all watersheds. The methodology and results outlined in this study will assist policymakers in accurately predicting nitrate and phosphorus concentration which will be instrumental in drafting a proper plan to deal with the problem of water pollution.
format	article
author	Aayush Bhattarai Sandeep Dhakal Yogesh Gautam Rabin Bhattarai
author_facet	Aayush Bhattarai Sandeep Dhakal Yogesh Gautam Rabin Bhattarai
author_sort	Aayush Bhattarai
title	Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse
title_short	Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse
title_full	Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse
title_fullStr	Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse
title_full_unstemmed	Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse
title_sort	prediction of nitrate and phosphorus concentrations using machine learning algorithms in watersheds with different landuse
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fe5f98314c92407590b8d9f81e95a1a4
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_version_	1718431386860257280

Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse

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