Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System
In this paper, the discharge coefficient of triangular labyrinth weir was predicted using multi-layer perceptron (MLP) neural network and Adaptive Neuro Fuzzy Inference System (ANFIS). To this purpose, 223 related dataset were collected. The Gamma Test (GT) was carried out to obtain the most affecti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2018-09-01
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| Series: | Alexandria Engineering Journal |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016817301679 |
| _version_ | 1819138834025152512 |
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| author | Amir Hamzeh Haghiabi Abbas Parsaie Samad Ememgholizadeh |
| author_facet | Amir Hamzeh Haghiabi Abbas Parsaie Samad Ememgholizadeh |
| author_sort | Amir Hamzeh Haghiabi |
| collection | DOAJ |
| description | In this paper, the discharge coefficient of triangular labyrinth weir was predicted using multi-layer perceptron (MLP) neural network and Adaptive Neuro Fuzzy Inference System (ANFIS). To this purpose, 223 related dataset were collected. The Gamma Test (GT) was carried out to obtain the most affective parameters on the discharge coefficient. The results of the GT indicated that the ratio of length of crest of weir to the main channel width Lw/Wmc, the ratio of length of one cycle to its width (Lc/Wc) and the ratio of total upstream head flow to the weir height H/P are the most important parameters. With regarding to the results of the GT, the structure of ANFIS model was designed. The results of ANFIS model with error indices including coefficient of determination value of 0.97 and root mean square error value of 0.03 was so suitable. Comparison the results of MLP with ANFIS model showed that both models has so suitable performance however the structure of ANFIS model is more optimal. Keywords: Labyrinth weir, Gamma Test, Discharge coefficient, ANFIS, ANNs |
| first_indexed | 2024-12-22T11:13:03Z |
| format | Article |
| id | doaj.art-763949d71d854b10aae0dddb456769c5 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-12-22T11:13:03Z |
| publishDate | 2018-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-763949d71d854b10aae0dddb456769c52022-12-21T18:28:05ZengElsevierAlexandria Engineering Journal1110-01682018-09-0157317731782Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference SystemAmir Hamzeh Haghiabi0Abbas Parsaie1Samad Ememgholizadeh2Water Engineering Department, Lorestan University, Khorramabad, Iran; Corresponding author.Water Engineering Department, Lorestan University, Khorramabad, IranDepartment of Water and Soil Engineering, Shahrood University of Technology, Shahrood, Semnan Province, IranIn this paper, the discharge coefficient of triangular labyrinth weir was predicted using multi-layer perceptron (MLP) neural network and Adaptive Neuro Fuzzy Inference System (ANFIS). To this purpose, 223 related dataset were collected. The Gamma Test (GT) was carried out to obtain the most affective parameters on the discharge coefficient. The results of the GT indicated that the ratio of length of crest of weir to the main channel width Lw/Wmc, the ratio of length of one cycle to its width (Lc/Wc) and the ratio of total upstream head flow to the weir height H/P are the most important parameters. With regarding to the results of the GT, the structure of ANFIS model was designed. The results of ANFIS model with error indices including coefficient of determination value of 0.97 and root mean square error value of 0.03 was so suitable. Comparison the results of MLP with ANFIS model showed that both models has so suitable performance however the structure of ANFIS model is more optimal. Keywords: Labyrinth weir, Gamma Test, Discharge coefficient, ANFIS, ANNshttp://www.sciencedirect.com/science/article/pii/S1110016817301679 |
| spellingShingle | Amir Hamzeh Haghiabi Abbas Parsaie Samad Ememgholizadeh Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System Alexandria Engineering Journal |
| title | Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System |
| title_full | Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System |
| title_fullStr | Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System |
| title_full_unstemmed | Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System |
| title_short | Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System |
| title_sort | prediction of discharge coefficient of triangular labyrinth weirs using adaptive neuro fuzzy inference system |
| url | http://www.sciencedirect.com/science/article/pii/S1110016817301679 |
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