Optimization of the water quality monitoring network in a basin with intensive agriculture using artificial intelligence algorithms
This research applies artificial intelligence algorithms for optimizing the water quality monitoring network in a representative basin with intensive agricultural and livestock activities. This study used the water quality database provided by the National Water Commission (CONAGUA). Bi-monthly moni...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2024-01-01
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| Series: | Water Supply |
| Subjects: | |
| Online Access: | http://ws.iwaponline.com/content/24/1/204 |
| _version_ | 1797200654313193472 |
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| author | Kimberly Mendivil-García José Luis Medina Héctor Rodríguez-Rangel Adriana Roé-Sosa Leonel Ernesto Amábilis-Sosa |
| author_facet | Kimberly Mendivil-García José Luis Medina Héctor Rodríguez-Rangel Adriana Roé-Sosa Leonel Ernesto Amábilis-Sosa |
| author_sort | Kimberly Mendivil-García |
| collection | DOAJ |
| description | This research applies artificial intelligence algorithms for optimizing the water quality monitoring network in a representative basin with intensive agricultural and livestock activities. This study used the water quality database provided by the National Water Commission (CONAGUA). Bi-monthly monitoring was registered from 2013 to 2020 for 23 water quality parameters in 23 sampling locations in tributaries and the mainstream river. Therefore, it was necessary to apply principal component analysis to reduce the dimensionality of the data and thus identify the parameters that contribute most to the variation in the water quality. This artificial intelligence algorithm promoted the ease of clustering sampling sites with similar water quality characteristics by reducing the number of variables involved in the database. The reduction highlighted nutrients (TN and TP), parameters related to dissolved organic matter (NH3-N and TOC), and pathogens such as fecal coliforms. The similarity of sampling sites was determined through hierarchical clustering using the Euclidean distance as a measure of dissimilarity and the Ward method as a grouping method. As a result, nine clusters were obtained for the rainy and dry seasons, reducing approximately 50% of the sampling sites and generating an optimized network of 11 sampling sites.
HIGHLIGHTS
The monitoring network of a watershed with intensive agriculture was reduced by 50% using artificial intelligence algorithms.;
By applying principal component analysis, the variables that contribute the most significant variation to the water quality of the basin, highlighting nutrients, and pathogens were identified.;
It was possible to agglomerate sampling sites according to their similarity in terms of water quality.; |
| first_indexed | 2024-03-08T00:26:42Z |
| format | Article |
| id | doaj.art-594017f8e09d439f842058cdae5fe113 |
| institution | Directory Open Access Journal |
| issn | 1606-9749 1607-0798 |
| language | English |
| last_indexed | 2024-04-24T07:35:05Z |
| publishDate | 2024-01-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Supply |
| spelling | doaj.art-594017f8e09d439f842058cdae5fe1132024-04-20T06:43:24ZengIWA PublishingWater Supply1606-97491607-07982024-01-0124120422210.2166/ws.2023.336336Optimization of the water quality monitoring network in a basin with intensive agriculture using artificial intelligence algorithmsKimberly Mendivil-García0José Luis Medina1Héctor Rodríguez-Rangel2Adriana Roé-Sosa3Leonel Ernesto Amábilis-Sosa4 División de Estudios de Posgrado e Investigación, CONACYT-Tecnológico Nacional de México/IT de Culiacán, Av. Juan de Dios Batiz, No. 310, 80220, Culiacán, Sinaloa, México División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de Culiacán, Av. Juan de Dios Batiz, No. 310, 80220, Culiacán, Sinaloa, México División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de Culiacán, Av. Juan de Dios Batiz, No. 310, 80220, Culiacán, Sinaloa, México Universidad Tecnológica de Culiacán, Carretera Imala km 2, C.P. 80014, Culiacán, Sinaloa, México División de Estudios de Posgrado e Investigación, CONACYT-Tecnológico Nacional de México/IT de Culiacán, Av. Juan de Dios Batiz, No. 310, 80220, Culiacán, Sinaloa, México This research applies artificial intelligence algorithms for optimizing the water quality monitoring network in a representative basin with intensive agricultural and livestock activities. This study used the water quality database provided by the National Water Commission (CONAGUA). Bi-monthly monitoring was registered from 2013 to 2020 for 23 water quality parameters in 23 sampling locations in tributaries and the mainstream river. Therefore, it was necessary to apply principal component analysis to reduce the dimensionality of the data and thus identify the parameters that contribute most to the variation in the water quality. This artificial intelligence algorithm promoted the ease of clustering sampling sites with similar water quality characteristics by reducing the number of variables involved in the database. The reduction highlighted nutrients (TN and TP), parameters related to dissolved organic matter (NH3-N and TOC), and pathogens such as fecal coliforms. The similarity of sampling sites was determined through hierarchical clustering using the Euclidean distance as a measure of dissimilarity and the Ward method as a grouping method. As a result, nine clusters were obtained for the rainy and dry seasons, reducing approximately 50% of the sampling sites and generating an optimized network of 11 sampling sites. HIGHLIGHTS The monitoring network of a watershed with intensive agriculture was reduced by 50% using artificial intelligence algorithms.; By applying principal component analysis, the variables that contribute the most significant variation to the water quality of the basin, highlighting nutrients, and pathogens were identified.; It was possible to agglomerate sampling sites according to their similarity in terms of water quality.;http://ws.iwaponline.com/content/24/1/204agricultural watershedartificial intelligenceland usesmonitoring networkoptimizationwater quality |
| spellingShingle | Kimberly Mendivil-García José Luis Medina Héctor Rodríguez-Rangel Adriana Roé-Sosa Leonel Ernesto Amábilis-Sosa Optimization of the water quality monitoring network in a basin with intensive agriculture using artificial intelligence algorithms Water Supply agricultural watershed artificial intelligence land uses monitoring network optimization water quality |
| title | Optimization of the water quality monitoring network in a basin with intensive agriculture using artificial intelligence algorithms |
| title_full | Optimization of the water quality monitoring network in a basin with intensive agriculture using artificial intelligence algorithms |
| title_fullStr | Optimization of the water quality monitoring network in a basin with intensive agriculture using artificial intelligence algorithms |
| title_full_unstemmed | Optimization of the water quality monitoring network in a basin with intensive agriculture using artificial intelligence algorithms |
| title_short | Optimization of the water quality monitoring network in a basin with intensive agriculture using artificial intelligence algorithms |
| title_sort | optimization of the water quality monitoring network in a basin with intensive agriculture using artificial intelligence algorithms |
| topic | agricultural watershed artificial intelligence land uses monitoring network optimization water quality |
| url | http://ws.iwaponline.com/content/24/1/204 |
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