Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case
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, accur...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/7/888 |
| _version_ | 1797540194446999552 |
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| author | Elizaveta Yudina Anna Petrovskaia Dmitrii Shadrin Polina Tregubova Elizaveta Chernova Mariia Pukalchik Ivan Oseledets |
| author_facet | Elizaveta Yudina Anna Petrovskaia Dmitrii Shadrin Polina Tregubova Elizaveta Chernova Mariia Pukalchik Ivan Oseledets |
| author_sort | Elizaveta Yudina |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:57:35Z |
| format | Article |
| id | doaj.art-eb85be425b7940edbef35f94c96d96d9 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T12:57:35Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-eb85be425b7940edbef35f94c96d96d92023-11-21T11:49:52ZengMDPI AGWater2073-44412021-03-0113788810.3390/w13070888Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use CaseElizaveta Yudina0Anna Petrovskaia1Dmitrii Shadrin2Polina Tregubova3Elizaveta Chernova4Mariia Pukalchik5Ivan Oseledets6Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 143026 Moscow, RussiaCenter for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 143026 Moscow, RussiaCenter for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 143026 Moscow, RussiaDigital Agriculture Laboratory, Skolkovo Institute of Science and Technology, 143026 Moscow, RussiaDigital Agriculture Laboratory, Skolkovo Institute of Science and Technology, 143026 Moscow, RussiaCenter for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 143026 Moscow, RussiaCenter for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 143026 Moscow, RussiaCurrently 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.https://www.mdpi.com/2073-4441/13/7/888water quality network optimizationgenetic algorithmvariable neighborhood searchwater quality indexgroundwater |
| spellingShingle | Elizaveta Yudina Anna Petrovskaia Dmitrii Shadrin Polina Tregubova Elizaveta Chernova Mariia Pukalchik Ivan Oseledets Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case Water water quality network optimization genetic algorithm variable neighborhood search water quality index groundwater |
| title | Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case |
| title_full | Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case |
| title_fullStr | Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case |
| title_full_unstemmed | Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case |
| title_short | Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case |
| title_sort | optimization of water quality monitoring networks using metaheuristic approaches moscow region use case |
| topic | water quality network optimization genetic algorithm variable neighborhood search water quality index groundwater |
| url | https://www.mdpi.com/2073-4441/13/7/888 |
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