A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessment
Under the dual influence of global climate change and human activities, river water environment is facing more and more serious problems and challenges. Assessing river water quality is of great significance for promoting regional sustainable development. Currently, traditional water quality assessm...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23003448 |
| _version_ | 1797837813067022336 |
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| author | Xi Yang Zhihe Chen |
| author_facet | Xi Yang Zhihe Chen |
| author_sort | Xi Yang |
| collection | DOAJ |
| description | Under the dual influence of global climate change and human activities, river water environment is facing more and more serious problems and challenges. Assessing river water quality is of great significance for promoting regional sustainable development. Currently, traditional water quality assessment methods usually do not consider the uncertainty of water quality data in the collection process, which limits the application of these methods. In order to overcome the above shortcomings, this study constructed a water quality assessment method by integrating Monte Carlo method (MC), CRITIC and VIKOR methods, and applied it to assess the quality of water in the Songhua River tributary. Results indicate that: (1) The water quality assessment of the two sampling points in the study area is level III, which is consistent with the actual situation; (2) This method can overcome the uncertainty caused by sampling error and improve the credibility of water quality evaluation results; (3) Total nitrogen (TN), potassium permanganate index (PPI) and ammonia nitrogen (NH3-N) are more evaluation factors related to the evaluation results. When the decision coefficient mechanism λ is taken [0.1–0.5], the outcomes are in line with the real quality of the water. In addition, we recommend that the distribution profile generated based on the measured data should obey the probability distribution density curve that decreases from the middle to the tail of both sides. The findings of this paper can provide a scientific basis for decision makers to carry out water quality restoration and management. |
| first_indexed | 2024-04-09T15:31:47Z |
| format | Article |
| id | doaj.art-fd2eaee039de44fc9ed6fc01c2a18f92 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-09T15:31:47Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-fd2eaee039de44fc9ed6fc01c2a18f922023-04-28T08:54:20ZengElsevierEcological Indicators1470-160X2023-06-01150110202A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessmentXi Yang0Zhihe Chen1School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China; Corresponding author at: School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China.Under the dual influence of global climate change and human activities, river water environment is facing more and more serious problems and challenges. Assessing river water quality is of great significance for promoting regional sustainable development. Currently, traditional water quality assessment methods usually do not consider the uncertainty of water quality data in the collection process, which limits the application of these methods. In order to overcome the above shortcomings, this study constructed a water quality assessment method by integrating Monte Carlo method (MC), CRITIC and VIKOR methods, and applied it to assess the quality of water in the Songhua River tributary. Results indicate that: (1) The water quality assessment of the two sampling points in the study area is level III, which is consistent with the actual situation; (2) This method can overcome the uncertainty caused by sampling error and improve the credibility of water quality evaluation results; (3) Total nitrogen (TN), potassium permanganate index (PPI) and ammonia nitrogen (NH3-N) are more evaluation factors related to the evaluation results. When the decision coefficient mechanism λ is taken [0.1–0.5], the outcomes are in line with the real quality of the water. In addition, we recommend that the distribution profile generated based on the measured data should obey the probability distribution density curve that decreases from the middle to the tail of both sides. The findings of this paper can provide a scientific basis for decision makers to carry out water quality restoration and management.http://www.sciencedirect.com/science/article/pii/S1470160X23003448Water quality assessmentMonte Carlo methodCRITICVIKOR methodSonghua river tributary |
| spellingShingle | Xi Yang Zhihe Chen A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessment Ecological Indicators Water quality assessment Monte Carlo method CRITIC VIKOR method Songhua river tributary |
| title | A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessment |
| title_full | A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessment |
| title_fullStr | A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessment |
| title_full_unstemmed | A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessment |
| title_short | A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessment |
| title_sort | hybrid approach based on monte carlo simulation vikor method for water quality assessment |
| topic | Water quality assessment Monte Carlo method CRITIC VIKOR method Songhua river tributary |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23003448 |
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