Numerical simulation of microcystin distribution in Liangxi River, downstream of Taihu Lake.
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| Title: | Numerical simulation of microcystin distribution in Liangxi River, downstream of Taihu Lake. |
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| Authors: | He, Xinchen1,2 (AUTHOR), Wang, Hua1,2 (AUTHOR) wanghua543543@163.com, Yan, Huaiyu1,2 (AUTHOR), Ao, Yanhui1,2 (AUTHOR) |
| Source: | Water Environment Research (10614303). Oct2021, Vol. 93 Issue 10, p1934-1943. 10p. |
| Subjects: | Water Environment Federation, Microcystins, Cyanobacterial toxins, Algal toxins, Computer simulation, Artificial neural networks, Algorithms |
| Abstract: | Microcystins (MCs), the algal toxins produced by cyanobacteria, raised a worldwide concern in recent decades. Limited monitoring stations for MCs make it hard to map the MC spatial distribution in certain areas. To tackle such problems, we selected Liangxi River as our research area and developed an integrated model to get spatial continuous MC data without too many sampling sites, which integrates a hydro‐environment model and an artificial neural network algorithm (ANN). The ANN algorithm can estimate concentration MCs via environmental factors. In this paper, we selected chl‐a, TN, TP, NO2‐, NO3‐, NH3‐N, and PO43‐ as stressors. The ANN model we established showed good performances both in train (R2 = 0.8407) and test set (R2 = 0.7543). In the hydro‐environment model, by inputting river geometry and model boundary data, the spatial continuous water quality data could be simulated. The water quality data returned from the hydro‐environmental model were used as input variables of the well‐trained ANN model; the continuous MC data were derived. To evaluate this model on geo‐mapping the MC distribution in Liangxi River, we compared the performance of this model and spatial interpolation on the test set, it turns out the integrated model showed a better performance. © 2020 Water Environment Federation Practitioner points: The cost of microcystin (MC) detection is too high for routine monitoring.We integrated regression method and hydro‐environment model to predict MCs.Results derived from spatial interpolation are not robust in unmonitored area.The new integration model can minimize the drawback of spatial interpolation. [ABSTRACT FROM AUTHOR] |
| Copyright of Water Environment Research (10614303) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 153050208 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Numerical simulation of microcystin distribution in Liangxi River, downstream of Taihu Lake. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22He%2C+Xinchen%22">He, Xinchen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Hua%22">Wang, Hua</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wanghua543543@163.com</i><br /><searchLink fieldCode="AR" term="%22Yan%2C+Huaiyu%22">Yan, Huaiyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ao%2C+Yanhui%22">Ao, Yanhui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Water+Environment+Research+%2810614303%29%22">Water Environment Research (10614303)</searchLink>. Oct2021, Vol. 93 Issue 10, p1934-1943. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Water+Environment+Federation%22">Water Environment Federation</searchLink><br /><searchLink fieldCode="DE" term="%22Microcystins%22">Microcystins</searchLink><br /><searchLink fieldCode="DE" term="%22Cyanobacterial+toxins%22">Cyanobacterial toxins</searchLink><br /><searchLink fieldCode="DE" term="%22Algal+toxins%22">Algal toxins</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Microcystins (MCs), the algal toxins produced by cyanobacteria, raised a worldwide concern in recent decades. Limited monitoring stations for MCs make it hard to map the MC spatial distribution in certain areas. To tackle such problems, we selected Liangxi River as our research area and developed an integrated model to get spatial continuous MC data without too many sampling sites, which integrates a hydro‐environment model and an artificial neural network algorithm (ANN). The ANN algorithm can estimate concentration MCs via environmental factors. In this paper, we selected chl‐a, TN, TP, NO2‐, NO3‐, NH3‐N, and PO43‐ as stressors. The ANN model we established showed good performances both in train (R2 = 0.8407) and test set (R2 = 0.7543). In the hydro‐environment model, by inputting river geometry and model boundary data, the spatial continuous water quality data could be simulated. The water quality data returned from the hydro‐environmental model were used as input variables of the well‐trained ANN model; the continuous MC data were derived. To evaluate this model on geo‐mapping the MC distribution in Liangxi River, we compared the performance of this model and spatial interpolation on the test set, it turns out the integrated model showed a better performance. © 2020 Water Environment Federation Practitioner points: The cost of microcystin (MC) detection is too high for routine monitoring.We integrated regression method and hydro‐environment model to predict MCs.Results derived from spatial interpolation are not robust in unmonitored area.The new integration model can minimize the drawback of spatial interpolation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Water Environment Research (10614303) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/wer.1484 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1934 Subjects: – SubjectFull: Water Environment Federation Type: general – SubjectFull: Microcystins Type: general – SubjectFull: Cyanobacterial toxins Type: general – SubjectFull: Algal toxins Type: general – SubjectFull: Computer simulation Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Numerical simulation of microcystin distribution in Liangxi River, downstream of Taihu Lake. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: He, Xinchen – PersonEntity: Name: NameFull: Wang, Hua – PersonEntity: Name: NameFull: Yan, Huaiyu – PersonEntity: Name: NameFull: Ao, Yanhui IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 10614303 Numbering: – Type: volume Value: 93 – Type: issue Value: 10 Titles: – TitleFull: Water Environment Research (10614303) Type: main |
| ResultId | 1 |
