Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models
Considering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (I...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/11/3714 |
| _version_ | 1797566875337490432 |
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| author | Ahmad Sharafati Masoud Haghbin Seyed Babak Haji Seyed Asadollah Nand Kumar Tiwari Nadhir Al-Ansari Zaher Mundher Yaseen |
| author_facet | Ahmad Sharafati Masoud Haghbin Seyed Babak Haji Seyed Asadollah Nand Kumar Tiwari Nadhir Al-Ansari Zaher Mundher Yaseen |
| author_sort | Ahmad Sharafati |
| collection | DOAJ |
| description | Considering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (IWO) and teaching learning based optimization (TLBO) are proposed to predict the maximum depth of scouring based on the different input combinations. Several performance indices and graphical evaluators are employed to estimate the prediction accuracy in the training and testing phase. Results show that the ANFIS-IWO offers the highest prediction performance (<i>RMSE</i> = 0.148) compared to other models in the testing phase, while the ANFIS-BBO (<i>RMSE</i> = 0.411) provides the lowest accuracy. The findings obtained from the uncertainty analysis of prediction modeling indicate that the input variables variability has a higher impact on the predicted results than the structure of models. In general, the ANFIS-IWO can be used as a reliable and cost-effective method for predicting the scouring depth downstream of weirs. |
| first_indexed | 2024-03-10T19:33:35Z |
| format | Article |
| id | doaj.art-4d3d821d518d4049a1fe55e6fa14a2a3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:33:35Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4d3d821d518d4049a1fe55e6fa14a2a32023-11-20T01:54:21ZengMDPI AGApplied Sciences2076-34172020-05-011011371410.3390/app10113714Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence ModelsAhmad Sharafati0Masoud Haghbin1Seyed Babak Haji Seyed Asadollah2Nand Kumar Tiwari3Nadhir Al-Ansari4Zaher Mundher Yaseen5Institute of Research and Development, Duy Tan University, Da Nang 550000, VietnamDepartment of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Civil Engineering, National Institute of Technology, Kurukshetra, Haryana, IndiaCivil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, SwedenSustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, VietnamConsidering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (IWO) and teaching learning based optimization (TLBO) are proposed to predict the maximum depth of scouring based on the different input combinations. Several performance indices and graphical evaluators are employed to estimate the prediction accuracy in the training and testing phase. Results show that the ANFIS-IWO offers the highest prediction performance (<i>RMSE</i> = 0.148) compared to other models in the testing phase, while the ANFIS-BBO (<i>RMSE</i> = 0.411) provides the lowest accuracy. The findings obtained from the uncertainty analysis of prediction modeling indicate that the input variables variability has a higher impact on the predicted results than the structure of models. In general, the ANFIS-IWO can be used as a reliable and cost-effective method for predicting the scouring depth downstream of weirs.https://www.mdpi.com/2076-3417/10/11/3714weirsscouring depthadaptive neuro-fuzzy inference systemsoptimization algorithms |
| spellingShingle | Ahmad Sharafati Masoud Haghbin Seyed Babak Haji Seyed Asadollah Nand Kumar Tiwari Nadhir Al-Ansari Zaher Mundher Yaseen Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models Applied Sciences weirs scouring depth adaptive neuro-fuzzy inference systems optimization algorithms |
| title | Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models |
| title_full | Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models |
| title_fullStr | Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models |
| title_full_unstemmed | Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models |
| title_short | Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models |
| title_sort | scouring depth assessment downstream of weirs using hybrid intelligence models |
| topic | weirs scouring depth adaptive neuro-fuzzy inference systems optimization algorithms |
| url | https://www.mdpi.com/2076-3417/10/11/3714 |
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