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Open AccessArticle

Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case

1
Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 143026 Moscow, Russia
2
Digital Agriculture Laboratory, Skolkovo Institute of Science and Technology, 143026 Moscow, Russia
3
Marchuk Institute of Numerical Mathematics, RAS, 119333 Moscow, Russia
*
Author to whom correspondence should be addressed.
Academic Editor: George Besseris
Water 2021, 13(7), 888; https://doi.org/10.3390/w13070888
Received: 18 February 2021 / Revised: 14 March 2021 / Accepted: 15 March 2021 / Published: 24 March 2021
(This article belongs to the Special Issue Water Quality Optimization)
Currently many countries are struggling to rationalize water quality monitoring stations which is caused by economic demand. Though this process is essential indeed, the exact elements of the system to be optimized without a subsequent quality and accuracy loss still remain obscure. Therefore, accurate historical data on groundwater pollution is required to detect and monitor considerable environmental impacts. To collect such data appropriate sampling and assessment methodologies with an optimum spatial distribution augmented should be exploited. Thus, the configuration of water monitoring sampling points and the number of the points required are now considered as a fundamental optimization challenge. The paper offers and tests metaheuristic approaches for optimization of monitoring procedure and multi-factors assessment of water quality in “New Moscow” area. It is shown that the considered algorithms allow us to reduce the size of the training sample set, so that the number of points for monitoring water quality in the area can be halved. Moreover, reducing the dataset size improved the quality of prediction by 20%. The obtained results convincingly demonstrate that the proposed algorithms dramatically decrease the total cost of analysis without dampening the quality of monitoring and could be recommended for optimization purposes. View Full-Text
Keywords: water quality network optimization; genetic algorithm; variable neighborhood search; water quality index; groundwater water quality network optimization; genetic algorithm; variable neighborhood search; water quality index; groundwater
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MDPI and ACS Style

Yudina, E.; Petrovskaia, A.; Shadrin, D.; Tregubova, P.; Chernova, E.; Pukalchik, M.; Oseledets, I. Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case. Water 2021, 13, 888. https://doi.org/10.3390/w13070888

AMA Style

Yudina E, Petrovskaia A, Shadrin D, Tregubova P, Chernova E, Pukalchik M, Oseledets I. Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case. Water. 2021; 13(7):888. https://doi.org/10.3390/w13070888

Chicago/Turabian Style

Yudina, Elizaveta; Petrovskaia, Anna; Shadrin, Dmitrii; Tregubova, Polina; Chernova, Elizaveta; Pukalchik, Mariia; Oseledets, Ivan. 2021. "Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case" Water 13, no. 7: 888. https://doi.org/10.3390/w13070888

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