Prediction of aeration efficiency of parshall and modified venturi flumes: application of soft computing versus regression models
Küçük Resim Yok
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Iwa Publishing
Erişim Hakkı
info:eu-repo/semantics/openAccess
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.
Açıklama
Anahtar Kelimeler
aeration efficiency, regression-based models, soft computing models
Kaynak
Water Supply
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
21
Sayı
8
