Modeling of discharge in compound open channels with convergent and divergent floodplains using soft computing methods
In this research, the estimation of discharge in compound open channels with convergent and divergent floodplains using soft computing methods, including the neural fuzzy group method of data handling (NF-GMDH), support vector regression (SVR), and M5 tree algorithm were performed. For this purpose,...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2023-09-01
|
| Series: | Journal of Hydroinformatics |
| Subjects: | |
| Online Access: | http://jhydro.iwaponline.com/content/25/5/1713 |
| _version_ | 1797661668395712512 |
|---|---|
| author | Sajad Bijanvand Mirali Mohammadi Abbas Parsaie Vishwanadham Mandala |
| author_facet | Sajad Bijanvand Mirali Mohammadi Abbas Parsaie Vishwanadham Mandala |
| author_sort | Sajad Bijanvand |
| collection | DOAJ |
| description | In this research, the estimation of discharge in compound open channels with convergent and divergent floodplains using soft computing methods, including the neural fuzzy group method of data handling (NF-GMDH), support vector regression (SVR), and M5 tree algorithm were performed. For this purpose, the geometric and hydraulic characteristics of the flow, including relative roughness (ff), relative area (Ar), relative hydraulic radius (Rr), relative dimension of the flow aspects (δ*), relative width (β), relative flow depth (Dr), relative longitudinal distance (Xr), convergent or divergent angle (θ) of the floodplain and longitudinal slope (So) of the bed were used as input variables and discharge was considered as the target (output) variable. The results showed that the statistical indices of the NF-GMDH in the testing stage are RMSENF-GMDH = 0.004, R2NF-GMDH = 0.923 and in the same stage for SVR are RMSESVR= 0.002 and R2SVR = 0.941 and finally for M5 tree algorithm are RMSEM5 = 0.002, R2M5= 0.931. The evaluation of the structure of the M5 tree algorithm showed that the most effective parameters are ff, Dr, Rr, δ*, and θ which confirm the important parameters specified by MARS, GMDH, and GEP algorithms used by previous researchers.
HIGHLIGHTS
Comparing the NF-GMDH, ANFIS, SVM, GEP, MARS, and M5 Algorithm for prediction of discharge in compound channels with convergent and divergent floodplains.; |
| first_indexed | 2024-03-11T18:48:58Z |
| format | Article |
| id | doaj.art-c09af2b9100448778acbedb0949c8a8b |
| institution | Directory Open Access Journal |
| issn | 1464-7141 1465-1734 |
| language | English |
| last_indexed | 2024-03-11T18:48:58Z |
| publishDate | 2023-09-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Hydroinformatics |
| spelling | doaj.art-c09af2b9100448778acbedb0949c8a8b2023-10-11T15:09:24ZengIWA PublishingJournal of Hydroinformatics1464-71411465-17342023-09-012551713172710.2166/hydro.2023.014014Modeling of discharge in compound open channels with convergent and divergent floodplains using soft computing methodsSajad Bijanvand0Mirali Mohammadi1Abbas Parsaie2Vishwanadham Mandala3 Department of Civil Engineering, Faculty of Engineering, Urmia University, Urmia, Iran. Department of Civil Engineering, Faculty of Engineering, Urmia University, Urmia, Iran. Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran Data Science, Indiana University, IU Bloomington 107 S. Indiana Avenue, Bloomington, IN 47405, USA In this research, the estimation of discharge in compound open channels with convergent and divergent floodplains using soft computing methods, including the neural fuzzy group method of data handling (NF-GMDH), support vector regression (SVR), and M5 tree algorithm were performed. For this purpose, the geometric and hydraulic characteristics of the flow, including relative roughness (ff), relative area (Ar), relative hydraulic radius (Rr), relative dimension of the flow aspects (δ*), relative width (β), relative flow depth (Dr), relative longitudinal distance (Xr), convergent or divergent angle (θ) of the floodplain and longitudinal slope (So) of the bed were used as input variables and discharge was considered as the target (output) variable. The results showed that the statistical indices of the NF-GMDH in the testing stage are RMSENF-GMDH = 0.004, R2NF-GMDH = 0.923 and in the same stage for SVR are RMSESVR= 0.002 and R2SVR = 0.941 and finally for M5 tree algorithm are RMSEM5 = 0.002, R2M5= 0.931. The evaluation of the structure of the M5 tree algorithm showed that the most effective parameters are ff, Dr, Rr, δ*, and θ which confirm the important parameters specified by MARS, GMDH, and GEP algorithms used by previous researchers. HIGHLIGHTS Comparing the NF-GMDH, ANFIS, SVM, GEP, MARS, and M5 Algorithm for prediction of discharge in compound channels with convergent and divergent floodplains.;http://jhydro.iwaponline.com/content/25/5/1713gmdhm5 algorithmsnonprismatic floodplainsupport vector machinetwo-stage channel |
| spellingShingle | Sajad Bijanvand Mirali Mohammadi Abbas Parsaie Vishwanadham Mandala Modeling of discharge in compound open channels with convergent and divergent floodplains using soft computing methods Journal of Hydroinformatics gmdh m5 algorithms nonprismatic floodplain support vector machine two-stage channel |
| title | Modeling of discharge in compound open channels with convergent and divergent floodplains using soft computing methods |
| title_full | Modeling of discharge in compound open channels with convergent and divergent floodplains using soft computing methods |
| title_fullStr | Modeling of discharge in compound open channels with convergent and divergent floodplains using soft computing methods |
| title_full_unstemmed | Modeling of discharge in compound open channels with convergent and divergent floodplains using soft computing methods |
| title_short | Modeling of discharge in compound open channels with convergent and divergent floodplains using soft computing methods |
| title_sort | modeling of discharge in compound open channels with convergent and divergent floodplains using soft computing methods |
| topic | gmdh m5 algorithms nonprismatic floodplain support vector machine two-stage channel |
| url | http://jhydro.iwaponline.com/content/25/5/1713 |
| work_keys_str_mv | AT sajadbijanvand modelingofdischargeincompoundopenchannelswithconvergentanddivergentfloodplainsusingsoftcomputingmethods AT miralimohammadi modelingofdischargeincompoundopenchannelswithconvergentanddivergentfloodplainsusingsoftcomputingmethods AT abbasparsaie modelingofdischargeincompoundopenchannelswithconvergentanddivergentfloodplainsusingsoftcomputingmethods AT vishwanadhammandala modelingofdischargeincompoundopenchannelswithconvergentanddivergentfloodplainsusingsoftcomputingmethods |