Modeling and Estimating the Dimensions of Stable Alluvial Channels using Soft Calculations
In this research, soft computational models including multiple adaptive spline regression model (MARS) and data group classification model (GMDH) were used to estimate the geometric dimensions of stable alluvial channels including channel surface width (w), flow depth (h), and longitudinal slope (S)...
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| Format: | Article |
| Language: | fas |
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Isfahan University of Technology
2022-12-01
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| Series: | علوم آب و خاک |
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| Online Access: | http://jstnar.iut.ac.ir/article-1-4199-en.html |
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| author | B. Shahinejad A. Parsaei A. Haghizadeh A. Arshia Z. Shamsi |
| author_facet | B. Shahinejad A. Parsaei A. Haghizadeh A. Arshia Z. Shamsi |
| author_sort | B. Shahinejad |
| collection | DOAJ |
| description | In this research, soft computational models including multiple adaptive spline regression model (MARS) and data group classification model (GMDH) were used to estimate the geometric dimensions of stable alluvial channels including channel surface width (w), flow depth (h), and longitudinal slope (S) and the results of the developed models were compared with the multilayer neural network (MLP) model. To develop the models, the flow rate parameters (Q), the average particle size in the floor and body (d50) as well as the shear stress (t) as input and the parameters of water surface width (w), flow depth (h), and longitudinal slope (S) were used as output parameters. Soft computing models were developed in two scenarios based on raw parameters and dimensionless form independent and dependent parameters. The results showed that the statistical characteristics in estimating w, the best performance is related to the MARS model, whose statistical indicators of accuracy in the training stage are R2 = 0.902, RMSE=1.666 and in the test phase is R2 = 0.844, RMSE=2.317. In estimating the channel depth, the performance of both GMDH and MARS models is approximately equal, both of which were developed based on the dimensionless form of flow rate as the input variable. The statistical indicators of both models in the training stage are R2 » 0.90, RMSE » 8.15 and in the test phase is R2 » 0.90, RMSE = 7.40. The best performance of the developed models in estimating the longitudinal slope of the channel was related to both MARS and GMDH models, although, in part, the accuracy of the GMDH model with statistical indicators R2 = 0.942, RMSE = 0.0011 in the training phase and R2 = 0.925, RMSE = 0.0014 in the experimental stage is more than the MARS model. |
| first_indexed | 2024-04-11T05:49:19Z |
| format | Article |
| id | doaj.art-d510b58d45f3407d93b28e1060ba3668 |
| institution | Directory Open Access Journal |
| issn | 2476-3594 2476-5554 |
| language | fas |
| last_indexed | 2024-04-11T05:49:19Z |
| publishDate | 2022-12-01 |
| publisher | Isfahan University of Technology |
| record_format | Article |
| series | علوم آب و خاک |
| spelling | doaj.art-d510b58d45f3407d93b28e1060ba36682022-12-22T04:42:08ZfasIsfahan University of Technologyعلوم آب و خاک2476-35942476-55542022-12-0126391107Modeling and Estimating the Dimensions of Stable Alluvial Channels using Soft CalculationsB. Shahinejad0A. Parsaei1A. Haghizadeh2A. Arshia3Z. Shamsi4 Lorestan University Shahid Chamran University Lorestan University LU Lorestan University In this research, soft computational models including multiple adaptive spline regression model (MARS) and data group classification model (GMDH) were used to estimate the geometric dimensions of stable alluvial channels including channel surface width (w), flow depth (h), and longitudinal slope (S) and the results of the developed models were compared with the multilayer neural network (MLP) model. To develop the models, the flow rate parameters (Q), the average particle size in the floor and body (d50) as well as the shear stress (t) as input and the parameters of water surface width (w), flow depth (h), and longitudinal slope (S) were used as output parameters. Soft computing models were developed in two scenarios based on raw parameters and dimensionless form independent and dependent parameters. The results showed that the statistical characteristics in estimating w, the best performance is related to the MARS model, whose statistical indicators of accuracy in the training stage are R2 = 0.902, RMSE=1.666 and in the test phase is R2 = 0.844, RMSE=2.317. In estimating the channel depth, the performance of both GMDH and MARS models is approximately equal, both of which were developed based on the dimensionless form of flow rate as the input variable. The statistical indicators of both models in the training stage are R2 » 0.90, RMSE » 8.15 and in the test phase is R2 » 0.90, RMSE = 7.40. The best performance of the developed models in estimating the longitudinal slope of the channel was related to both MARS and GMDH models, although, in part, the accuracy of the GMDH model with statistical indicators R2 = 0.942, RMSE = 0.0011 in the training phase and R2 = 0.925, RMSE = 0.0014 in the experimental stage is more than the MARS model.http://jstnar.iut.ac.ir/article-1-4199-en.htmlartificial neural networkgmdh modelmars modelerosion channelintelligent regression model |
| spellingShingle | B. Shahinejad A. Parsaei A. Haghizadeh A. Arshia Z. Shamsi Modeling and Estimating the Dimensions of Stable Alluvial Channels using Soft Calculations علوم آب و خاک artificial neural network gmdh model mars model erosion channel intelligent regression model |
| title | Modeling and Estimating the Dimensions of Stable Alluvial Channels using Soft Calculations |
| title_full | Modeling and Estimating the Dimensions of Stable Alluvial Channels using Soft Calculations |
| title_fullStr | Modeling and Estimating the Dimensions of Stable Alluvial Channels using Soft Calculations |
| title_full_unstemmed | Modeling and Estimating the Dimensions of Stable Alluvial Channels using Soft Calculations |
| title_short | Modeling and Estimating the Dimensions of Stable Alluvial Channels using Soft Calculations |
| title_sort | modeling and estimating the dimensions of stable alluvial channels using soft calculations |
| topic | artificial neural network gmdh model mars model erosion channel intelligent regression model |
| url | http://jstnar.iut.ac.ir/article-1-4199-en.html |
| work_keys_str_mv | AT bshahinejad modelingandestimatingthedimensionsofstablealluvialchannelsusingsoftcalculations AT aparsaei modelingandestimatingthedimensionsofstablealluvialchannelsusingsoftcalculations AT ahaghizadeh modelingandestimatingthedimensionsofstablealluvialchannelsusingsoftcalculations AT aarshia modelingandestimatingthedimensionsofstablealluvialchannelsusingsoftcalculations AT zshamsi modelingandestimatingthedimensionsofstablealluvialchannelsusingsoftcalculations |