Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks.
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| Title: | Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks. |
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| Authors: | Hu, Xiande1 (AUTHOR) huxiande@axhu.edu.cn, Gu, Fenfei1 (AUTHOR), Fan, Xueping1 (AUTHOR) fanxp@lzu.edu.cn |
| Source: | Advances in Civil Engineering. 10/22/2024, Vol. 2024, p1-14. 14p. |
| Subjects: | Artificial neural networks, Computer input design, Particle swarm optimization, Bayesian analysis, Nonlinear equations |
| Abstract: | Waterlogging warning has gradually become an important means of urban waterlogging prevention and control. However, the current urban waterlogging warning model still has problems such as low accuracy, real‐time performance, and poor model convergence. To better address these issues, this article combines particle swarm optimization (PSO) and deep neural networks (DNN) to explore the construction of early warning models in depth. First, the influencing factors of urban waterlogging were analyzed in the article, and the PSO algorithm was used to determine the influencing factors of urban waterlogging in this study; then, the selected influencing factors were used as input data to design a backpropagation (BP) neural network (NN) structure; several representative waterlogging points can be selected to construct a BP NN model and perform fitting analysis. Afterward, combined with the PSO algorithm, the constructed model was trained and optimized. In this article, the old town of Hefei City is used as the experimental object, and the building model is used to conduct early warning research on waterlogging. The study's findings indicate that the PSO + BP model's average accuracy in 10 early warning tests is as high as 97.95%, with a response time of only 0.022 ms; the average accuracy and response time of the BP model are 89.06% and 0.255 ms, respectively; the Bayesian network model (BN model) is 82.78% and 0.275 ms. Through the analysis of actual cases in the old urban area of Hefei City, the advantages of this model in practical application were verified, and a new intelligent warning method for urban waterlogging prevention and control was provided, demonstrating its effectiveness and potential in dealing with complex nonlinear problems. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Civil Engineering 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 180410603 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hu%2C+Xiande%22">Hu, Xiande</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> huxiande@axhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Gu%2C+Fenfei%22">Gu, Fenfei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fan%2C+Xueping%22">Fan, Xueping</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fanxp@lzu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Advances+in+Civil+Engineering%22">Advances in Civil Engineering</searchLink>. 10/22/2024, Vol. 2024, p1-14. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+input+design%22">Computer input design</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+equations%22">Nonlinear equations</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Waterlogging warning has gradually become an important means of urban waterlogging prevention and control. However, the current urban waterlogging warning model still has problems such as low accuracy, real‐time performance, and poor model convergence. To better address these issues, this article combines particle swarm optimization (PSO) and deep neural networks (DNN) to explore the construction of early warning models in depth. First, the influencing factors of urban waterlogging were analyzed in the article, and the PSO algorithm was used to determine the influencing factors of urban waterlogging in this study; then, the selected influencing factors were used as input data to design a backpropagation (BP) neural network (NN) structure; several representative waterlogging points can be selected to construct a BP NN model and perform fitting analysis. Afterward, combined with the PSO algorithm, the constructed model was trained and optimized. In this article, the old town of Hefei City is used as the experimental object, and the building model is used to conduct early warning research on waterlogging. The study's findings indicate that the PSO + BP model's average accuracy in 10 early warning tests is as high as 97.95%, with a response time of only 0.022 ms; the average accuracy and response time of the BP model are 89.06% and 0.255 ms, respectively; the Bayesian network model (BN model) is 82.78% and 0.275 ms. Through the analysis of actual cases in the old urban area of Hefei City, the advantages of this model in practical application were verified, and a new intelligent warning method for urban waterlogging prevention and control was provided, demonstrating its effectiveness and potential in dealing with complex nonlinear problems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Advances in Civil Engineering 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: 10.1155/2024/9601590 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Computer input design Type: general – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Nonlinear equations Type: general Titles: – TitleFull: Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hu, Xiande – PersonEntity: Name: NameFull: Gu, Fenfei – PersonEntity: Name: NameFull: Fan, Xueping IsPartOfRelationships: – BibEntity: Dates: – D: 22 M: 10 Text: 10/22/2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 16878086 Numbering: – Type: volume Value: 2024 Titles: – TitleFull: Advances in Civil Engineering Type: main |
| ResultId | 1 |
