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Article

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

1
Department of Mechanical and Aerospace Engineering, Institute of Engineering, Pulchowk Campus, Tribhuvan University, Kathmandu 44700, Nepal
2
Department of Agricultural and Biological Engineering, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Academic Editor: David Post
Water 2021, 13(21), 3096; https://doi.org/10.3390/w13213096
Received: 15 October 2021 / Revised: 31 October 2021 / Accepted: 1 November 2021 / Published: 3 November 2021
(This article belongs to the Special Issue Water Quality Management of Inland Waters)
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. View Full-Text
Keywords: nitrate concentration; phosphorus concentration; machine learning; Bayesian optimization; water pollution nitrate concentration; phosphorus concentration; machine learning; Bayesian optimization; water pollution
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MDPI and ACS Style

Bhattarai, A.; Dhakal, S.; Gautam, Y.; Bhattarai, R. Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse. Water 2021, 13, 3096. https://doi.org/10.3390/w13213096

AMA Style

Bhattarai A, Dhakal S, Gautam Y, Bhattarai R. Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse. Water. 2021; 13(21):3096. https://doi.org/10.3390/w13213096

Chicago/Turabian Style

Bhattarai, Aayush, Sandeep Dhakal, Yogesh Gautam, and Rabin Bhattarai. 2021. "Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse" Water 13, no. 21: 3096. https://doi.org/10.3390/w13213096

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