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

Abstract

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) 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 AE20 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 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 at Parshall and Modified Venturi flumes.

Environmental Quality Management through Parshall Flume Aeration Efficiency Modelling

“…Six dominant independent variables are selected to investigate the eff ect of Parshall fl ume characteristics and confi gurations on its aeration effi ciency (A E20 ) as dependent variables: Parshall discharge (Q), throat widths (B), throat lengths (G), sill heights (K), oxygen defi cit ratio (O g ), and exponent (f), (Chau et al, 2021 andChauhane et al, 2021). The data set consists of 96 observations were used and obtained from the laboratory experiments.…”

Analytical Investigation of the Impact of Jet Geometry on Aeration Effectiveness Using Soft Computing Techniques

“…PSO, GA, and hybrid ANN algorithms with HHO models have been extensively employed for studying hydraulic formations, showcasing exceptional efficacy in simulating the scouring depth downstream of a spillway caused by ski-jump flows. 34 A study was also carried out to predict the E 20 in Parshall and Venturi flumes using a range of 35 Their findings demonstrated that the MARS model is the superior predictor. A single plunging jet's oxygen transfer rate in turbulent crossflow was measured using two distinct models and the complex behavior of the two-phase air−water flow was investigated using the flow visualization technique.…”

“…PSO, GA, and hybrid ANN algorithms with HHO models have been extensively employed for studying hydraulic formations, showcasing exceptional efficacy in simulating the scouring depth downstream of a spillway caused by ski-jump flows . A study was also carried out to predict the E 20 in Parshall and Venturi flumes using a range of soft computing algorithms, including RF, tree-based M5P, GMDH, and MARS . Their findings demonstrated that the MARS model is the superior predictor.…”

Estimating the permeability coefficient of soil using CART and GMDH approaches

“…Samadi et al (2021aSamadi et al ( , 2021b indicated that CART is more convenient for modeling dynamic pressure distribution in hydraulic structures. Sihag et al (2021aSihag et al ( , 2021b) simulated soil infiltration rate using regression tree methods.…”

“…Khedri et al (2020) used GMDH to estimate groundwater levels. Sihag et al (2021aSihag et al ( , 2021b predicted the aeration efficiency of the flume using GMDH. Nasrabadi et al (2021) used the GMDH method to predict submerged hydraulic jump characteristics such as jump length, relative energy loss and relative submergence depth.…”