A multi-collaborative technique for assessing the reservoir sedimentation with futuristic capacity prediction using ANN model.
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| Title: | A multi-collaborative technique for assessing the reservoir sedimentation with futuristic capacity prediction using ANN model. |
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| Authors: | Mishra, Kartikeya1 (AUTHOR) mishra1999kartikeya@gmail.com, Tiwari, H. L.1 (AUTHOR) |
| Source: | Environmental Earth Sciences. 7/15/2024, Vol. 83 Issue 14, p1-12. 12p. |
| Subjects: | Sedimentation analysis, Artificial neural networks, Reservoir sedimentation, Sedimentation & deposition, Geographic information systems |
| Abstract: | This study illustrates the comprehensive investigation to assess the sedimentation, deposition pattern, and futuristic active capacity of the reservoir in a minimal period. A multi-collaborative methodology was developed using Geographic information system (GIS) & Artificial Neural Networks (ANN). The sedimentation analysis was carried out on a GIS environment using satellite rasters. The satellite data captures the live water region with a combination of visible and Near-Infrared (NIR) bands. The ANN model was developed using feed-forward backpropagation algorithm to forecast the revised water spread. A Multi-Layer Perceptron [2-1(10)-1(1)-1] ANN structure best captures the trend of water-spread reduction with the coefficient of determination (R2)1,1,1 & 0.977 for training, testing, validation, and overall performance respectively. The designed approach provides a performance comparison of GIS and ANN methods for the prediction of reservoir capacity. Also, the observations of sedimentation analysis were superimposed on the Borland & Miller graph to portray the pattern of deposition. The research framework was applied to the Kerwan reservoir located in the capital of central India. This analysis reveals that the useful capacity of the reservoir had reduced from 22.67 to 15.13 Mm3 in 46 years (1976–2022) and the depositing pattern was shifting towards Type-II (Flood Plain-Foot Hill) which was designed as Type-III like Hilly reservoir. From the Neural Network fittings, it was concluded that Kerwan would be suppressed to 59.95% in 2030 and reduced up to 49.49% & 40.84% for 2050 & 2070 respectively, if siltation carried on. [ABSTRACT FROM AUTHOR] |
| Copyright of Environmental Earth Sciences is the property of Springer Nature 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 178969527 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A multi-collaborative technique for assessing the reservoir sedimentation with futuristic capacity prediction using ANN model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mishra%2C+Kartikeya%22">Mishra, Kartikeya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mishra1999kartikeya@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Tiwari%2C+H%2E+L%2E%22">Tiwari, H. L.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Earth+Sciences%22">Environmental Earth Sciences</searchLink>. 7/15/2024, Vol. 83 Issue 14, p1-12. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sedimentation+analysis%22">Sedimentation analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Reservoir+sedimentation%22">Reservoir sedimentation</searchLink><br /><searchLink fieldCode="DE" term="%22Sedimentation+%26+deposition%22">Sedimentation & deposition</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+information+systems%22">Geographic information systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study illustrates the comprehensive investigation to assess the sedimentation, deposition pattern, and futuristic active capacity of the reservoir in a minimal period. A multi-collaborative methodology was developed using Geographic information system (GIS) & Artificial Neural Networks (ANN). The sedimentation analysis was carried out on a GIS environment using satellite rasters. The satellite data captures the live water region with a combination of visible and Near-Infrared (NIR) bands. The ANN model was developed using feed-forward backpropagation algorithm to forecast the revised water spread. A Multi-Layer Perceptron [2-1(10)-1(1)-1] ANN structure best captures the trend of water-spread reduction with the coefficient of determination (R2)1,1,1 & 0.977 for training, testing, validation, and overall performance respectively. The designed approach provides a performance comparison of GIS and ANN methods for the prediction of reservoir capacity. Also, the observations of sedimentation analysis were superimposed on the Borland & Miller graph to portray the pattern of deposition. The research framework was applied to the Kerwan reservoir located in the capital of central India. This analysis reveals that the useful capacity of the reservoir had reduced from 22.67 to 15.13 Mm3 in 46 years (1976–2022) and the depositing pattern was shifting towards Type-II (Flood Plain-Foot Hill) which was designed as Type-III like Hilly reservoir. From the Neural Network fittings, it was concluded that Kerwan would be suppressed to 59.95% in 2030 and reduced up to 49.49% & 40.84% for 2050 & 2070 respectively, if siltation carried on. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environmental Earth Sciences is the property of Springer Nature 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: 10.1007/s12665-024-11757-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Sedimentation analysis Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Reservoir sedimentation Type: general – SubjectFull: Sedimentation & deposition Type: general – SubjectFull: Geographic information systems Type: general Titles: – TitleFull: A multi-collaborative technique for assessing the reservoir sedimentation with futuristic capacity prediction using ANN model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mishra, Kartikeya – PersonEntity: Name: NameFull: Tiwari, H. L. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: 7/15/2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 18666280 Numbering: – Type: volume Value: 83 – Type: issue Value: 14 Titles: – TitleFull: Environmental Earth Sciences Type: main |
| ResultId | 1 |
