Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms
Flow through open channels can contain solids. The deposition of solids occasionally occurs due to insufficient flow velocity to transfer the solid particles, causing many problems with transfer systems. Therefore, a method to determine the limiting velocity (i.e. Fr) is required. In this paper, thr...
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| Format: | Article |
| Language: | English |
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Razi University
2017-06-01
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| Series: | Journal of Applied Research in Water and Wastewater |
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| Online Access: | https://arww.razi.ac.ir/article_773_d2ca3120c7e73d1b4bafcfe1163cb8bf.pdf |
| _version_ | 1818390154553851904 |
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| author | Sultan Noman Qasem Isa Ebtehaj Hossien Riahi Madavar |
| author_facet | Sultan Noman Qasem Isa Ebtehaj Hossien Riahi Madavar |
| author_sort | Sultan Noman Qasem |
| collection | DOAJ |
| description | Flow through open channels can contain solids. The deposition of solids occasionally occurs due to insufficient flow velocity to transfer the solid particles, causing many problems with transfer systems. Therefore, a method to determine the limiting velocity (i.e. Fr) is required. In this paper, three alternative, hybrid evolutionary algorithm methods, including differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) based on the adaptive network-based fuzzy inference system are presented: ANFIS-GA, ANFIS-DE and ANFIS-PSO. In these methods, evolutionary algorithms optimize the membership functions, and ANFIS adjusts the premises and consequent parameters to optimize prediction performance. The performance of the proposed methods is compared with that of the general ANFIS using three different datasets comprising a wide range of data. The results show that the hybrid models (ANFIS-GA, ANFIS-DE and ANFIS-PSO) are more accurate than general ANFIS in training with a hybrid algorithm (hybrid of back propagation and least squares). Among the evolutionary algorithms, ANFIS-PSO performed the best (R2=0.976, RMSE=0.26, MARE=0.057, BIAS=-0.004 and SI=0.059). |
| first_indexed | 2024-12-14T04:53:07Z |
| format | Article |
| id | doaj.art-01d1cfb54d824c8ca6e5102cc7d4520c |
| institution | Directory Open Access Journal |
| issn | 2476-6283 2476-6283 |
| language | English |
| last_indexed | 2024-12-14T04:53:07Z |
| publishDate | 2017-06-01 |
| publisher | Razi University |
| record_format | Article |
| series | Journal of Applied Research in Water and Wastewater |
| spelling | doaj.art-01d1cfb54d824c8ca6e5102cc7d4520c2022-12-21T23:16:29ZengRazi UniversityJournal of Applied Research in Water and Wastewater2476-62832476-62832017-06-014129029810.22126/arww.2017.773773Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithmsSultan Noman Qasem0Isa Ebtehaj1Hossien Riahi Madavar2Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud, Islamic University (IMSIU), Riyadh, Saudi ArabiaYoung Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, IranDepartment of water engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, IranFlow through open channels can contain solids. The deposition of solids occasionally occurs due to insufficient flow velocity to transfer the solid particles, causing many problems with transfer systems. Therefore, a method to determine the limiting velocity (i.e. Fr) is required. In this paper, three alternative, hybrid evolutionary algorithm methods, including differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) based on the adaptive network-based fuzzy inference system are presented: ANFIS-GA, ANFIS-DE and ANFIS-PSO. In these methods, evolutionary algorithms optimize the membership functions, and ANFIS adjusts the premises and consequent parameters to optimize prediction performance. The performance of the proposed methods is compared with that of the general ANFIS using three different datasets comprising a wide range of data. The results show that the hybrid models (ANFIS-GA, ANFIS-DE and ANFIS-PSO) are more accurate than general ANFIS in training with a hybrid algorithm (hybrid of back propagation and least squares). Among the evolutionary algorithms, ANFIS-PSO performed the best (R2=0.976, RMSE=0.26, MARE=0.057, BIAS=-0.004 and SI=0.059).https://arww.razi.ac.ir/article_773_d2ca3120c7e73d1b4bafcfe1163cb8bf.pdfANFISDifferential Evolution (DE)Genetic Algorithm (GA)non-deposition sediment transportParticle Swarm Optimization (PSO) |
| spellingShingle | Sultan Noman Qasem Isa Ebtehaj Hossien Riahi Madavar Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms Journal of Applied Research in Water and Wastewater ANFIS Differential Evolution (DE) Genetic Algorithm (GA) non-deposition sediment transport Particle Swarm Optimization (PSO) |
| title | Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms |
| title_full | Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms |
| title_fullStr | Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms |
| title_full_unstemmed | Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms |
| title_short | Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms |
| title_sort | optimizing anfis for sediment transport in open channels using different evolutionary algorithms |
| topic | ANFIS Differential Evolution (DE) Genetic Algorithm (GA) non-deposition sediment transport Particle Swarm Optimization (PSO) |
| url | https://arww.razi.ac.ir/article_773_d2ca3120c7e73d1b4bafcfe1163cb8bf.pdf |
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