Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models | Zendy

Parveen Sihag | Zendy; Ö. Faruk Dursun | Zendy; Saad Sh. Sammen | Zendy; Anurag Malik | Zendy; Anita Chauhan | Zendy

Open Access

Prediction of aeration efficiency of Parshall and Modified Venturi flumes: application of soft computing versus regression models

Author(s) -

Parveen Sihag,

Ö. Faruk Dursun,

Saad Sh. Sammen,

Anurag Malik,

Anita Chauhan

Publication year - 2021

Publication title -

water science and technology water supply

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.318

H-Index - 39

eISSN - 1607-0798

pISSN - 1606-9749

DOI - 10.2166/ws.2021.161

Subject(s) - venturi effect , multivariate adaptive regression splines , mean squared error , soft computing , linear regression , soft sensor , regression analysis , statistics , coefficient of determination , mathematics , mars exploration program , regression , sensitivity (control systems) , correlation coefficient , engineering , bayesian multivariate linear regression , computer science , machine learning , artificial neural network , process (computing) , mechanical engineering , physics , astronomy , inlet , operating system , electronic engineering

In this study, the potential of soft computing techniques, namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH), was evaluated to predict the aeration efficiency (AE20) of Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models i.e., with multiple linear regression (MLR) and multiple nonlinear regression (MNLR). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting AE20 of Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) = 0.9997, and 0.9992, and root mean square error (RMSE) = 0.0015, and 0.0045 during calibration and validation periods, respectively. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE20 of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE20 of Parshall and Modified Venturi flumes.

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