Prediction of aeration efficiency of parshall and modified venturi flumes: application of soft computing versus regression models

Sihag P., DURSUN Ö. F., Sammen S. S., Malik A., Chauhan A.

WATER SUPPLY, cilt.21, sa.8, ss.4068-4085, 2021 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 21 Sayı: 8
  • Basım Tarihi: 2021
  • Doi Numarası: 10.2166/ws.2021.161
  • Dergi Adı: WATER SUPPLY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, EMBASE, Environment Index, Geobase, ICONDA Bibliographic, Pollution Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Sayfa Sayıları: ss.4068-4085
  • Anahtar Kelimeler: aeration efficiency, regression-based models, soft computing models, AIR ENTRAINMENT, PERFORMANCE, SIMULATION, WEIRS
  • İnönü Üniversitesi Adresli: Evet

Özet

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 (AE(20)) at 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., MLR: multiple linear regression, and MNLR: multiple nonlinear regression). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting the AE(20) at 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. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE(20) of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE(20) at Parshall and Modified Venturi flumes.