A Hybrid ANFIS- PSO Model for Scour Depth Prediction
In recent years, newly-developed data mining and machine learning techniques have been applied in various fields to build intelligent information systems. However, few of these approaches offer online support or are flexibleto be adapted to large and complex datasets. Therefore, the present research w...
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| Format: | Article |
| Language: | fas |
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Iranian Rainwater Catchment Systems Association
2016-01-01
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| Series: | محیط زیست و مهندسی آب |
| Subjects: | |
| Online Access: | http://www.jewe.ir/article_12318_2cb181722c4948c236c05ce4ae0119dd.pdf |
| _version_ | 1797783564633243648 |
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| author | Mohammad Heman Jannaty Afshin Eghbalzadeh SeyedAbbas Hosseini |
| author_facet | Mohammad Heman Jannaty Afshin Eghbalzadeh SeyedAbbas Hosseini |
| author_sort | Mohammad Heman Jannaty |
| collection | DOAJ |
| description | In recent years, newly-developed data mining and machine learning techniques have been applied in various fields to build intelligent information systems. However, few of these approaches offer online support or are flexibleto be adapted to large and complex datasets. Therefore, the present research work adopts Particle Swarm Optimization (PSO) techniques to obtain appropriate parameter settings for membership function and integrates the Adaptive-Network-based Fuzzy Inference System (ANFIS) model to make the model fit for predicting scour depth. A dataset of 188 scour depths for single piers presented by the USGS was used. Results of the model prediction show that the derived model is best fitted to the field data. The proposed one-order momentum method is able to learn quickly through one-pass training and provides high-accuracy short-term predictions. Moreover, this method is suitable for online learning but the two-order momentum method is appropriate for incremental learning. The PSO-ANFIS approach could provide better results in predicting scour depths compared with other models. |
| first_indexed | 2024-03-13T00:27:41Z |
| format | Article |
| id | doaj.art-05c488709d2043c08c0d5fb827136b95 |
| institution | Directory Open Access Journal |
| issn | 2476-3683 |
| language | fas |
| last_indexed | 2024-03-13T00:27:41Z |
| publishDate | 2016-01-01 |
| publisher | Iranian Rainwater Catchment Systems Association |
| record_format | Article |
| series | محیط زیست و مهندسی آب |
| spelling | doaj.art-05c488709d2043c08c0d5fb827136b952023-07-11T03:46:43ZfasIranian Rainwater Catchment Systems Associationمحیط زیست و مهندسی آب2476-36832016-01-0111819412318A Hybrid ANFIS- PSO Model for Scour Depth PredictionMohammad Heman Jannaty0Afshin Eghbalzadeh1SeyedAbbas Hosseini2Ph.D. Scholar, Department of Civil Engineering, Faculty of Civil Engineering, Islamic Azad University, Science and Research Branch, Tehran, IranAssist. Professor, Department of Civil Engineering, Faculty of Civil Engineering, University of Razi, Kermanshah, IranAssist. Professor, Department of Civil Engineering, Faculty of Civil Engineering, Islamic Azad University, Science and Research Branch, Tehran, iranIn recent years, newly-developed data mining and machine learning techniques have been applied in various fields to build intelligent information systems. However, few of these approaches offer online support or are flexibleto be adapted to large and complex datasets. Therefore, the present research work adopts Particle Swarm Optimization (PSO) techniques to obtain appropriate parameter settings for membership function and integrates the Adaptive-Network-based Fuzzy Inference System (ANFIS) model to make the model fit for predicting scour depth. A dataset of 188 scour depths for single piers presented by the USGS was used. Results of the model prediction show that the derived model is best fitted to the field data. The proposed one-order momentum method is able to learn quickly through one-pass training and provides high-accuracy short-term predictions. Moreover, this method is suitable for online learning but the two-order momentum method is appropriate for incremental learning. The PSO-ANFIS approach could provide better results in predicting scour depths compared with other models.http://www.jewe.ir/article_12318_2cb181722c4948c236c05ce4ae0119dd.pdfscour depthpso-anfisfield datasingle piers |
| spellingShingle | Mohammad Heman Jannaty Afshin Eghbalzadeh SeyedAbbas Hosseini A Hybrid ANFIS- PSO Model for Scour Depth Prediction محیط زیست و مهندسی آب scour depth pso-anfis field data single piers |
| title | A Hybrid ANFIS- PSO Model for Scour Depth Prediction |
| title_full | A Hybrid ANFIS- PSO Model for Scour Depth Prediction |
| title_fullStr | A Hybrid ANFIS- PSO Model for Scour Depth Prediction |
| title_full_unstemmed | A Hybrid ANFIS- PSO Model for Scour Depth Prediction |
| title_short | A Hybrid ANFIS- PSO Model for Scour Depth Prediction |
| title_sort | hybrid anfis pso model for scour depth prediction |
| topic | scour depth pso-anfis field data single piers |
| url | http://www.jewe.ir/article_12318_2cb181722c4948c236c05ce4ae0119dd.pdf |
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