Prediction of oxygen transfer at modified Parshall flumes using regression models

Tiwari, N. K.; Sihag, Parveen

doi:10.1080/09715010.2018.1473058

Cited by 26 publications

(17 citation statements)

References 36 publications

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“…For last recent years, the soft computing tools-based methods have been the most widely applied in estimation of intricate phenomena in discipline of environmental and water resources engineering and reported to be very efficient [14,15,18,23,24,26,28]. Utilizing soft computing methods, ANN, FL and ANFIS model, E 20 for Parshal and modified Parshal flumes was suggested by Tiwari and Sihag [27].…”

Section: Objective and Novelty In Studymentioning

confidence: 99%

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Evaluating Parshall flume aeration with experimental observations and advance soft computing techniques

Bhoria

Sihag

Singh³

et al. 2021

Neural Comput & Applic

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A Parshall flume is a versatile device due to its diverse applications in irrigation canals, mine discharge, dam seepage, sewage treatment plants and many more. It is an economical due to its construction and installation and an accurate in discharge measurement in open channel and non-full pipe. Exchange of oxygen between water and air is termed as aeration. In the study, advance soft computing models; Multivariate Adaptive Regression Splines (MARS) and Generalized Structure-Group Method of Data Handling (GS-GMDH) have used to predict aeration efficiency (E 20 ) values at Parshall flumes and its modified forms and also compared with existing conventional models. The performance of the models was assessed using four evaluating metrics; coefficient of determination (R 2 ), mean absolute error (MAE), root mean square error (RMSE) and Nash Sutcliffe model efficacy. Agreement plot of GS-GMDH and MARS models showed that the MARS model has the maximum exactness in predicting E 20 values at Parshall flumes and its modified forms with the lowest RMSE = 0.0020, MAE = 0.0015. MARS model with input combination of ratio of throat width to throat length (W/ L), ratio of sill height to throat length (S/L) and Froude number is performing better than that of all other input combinations used for models and found to be more suitable for predicting the E 20 . Overall comparison among conventional and advance soft computing models suggests that advance soft computing models perform better than conventional models.

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Section: Objective and Novelty In Studymentioning

confidence: 99%

“…Tiwari and Sihag [27], Dursun [6] performed experiments on aeration efficiency at Parshall flumes with array of experiments. Baylar et al [3], Emiroglu and Baylar [8] assessed the aeration potential of stepped-channel without and with end sills, respectively.…”

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confidence: 99%

Evaluating Parshall flume aeration with experimental observations and advance soft computing techniques

Bhoria

Sihag

Singh³

et al. 2021

Neural Comput & Applic

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“…As the photocatalytic process is somewhat affected by different parameters, simulating and modelling based on conventional mathematical approaches are quite complicated [36]. In this context, an adaptive neuro-fuzzy inference system (ANFIS) allows for the prediction of water quality parameters, as has been previously demonstrated [37,38]. Additionally, the application of an ANFIS model has been reported to evaluate wastewater treatment processes, namely in the elimination of organic dyes [39,40], oily wastewater [41], chemical additives [36], antibiotics [42] and heavy metals [43,44].…”

Section: Introductionmentioning

confidence: 99%

Reusable Ag@TiO2-Based Photocatalytic Nanocomposite Membranes for Solar Degradation of Contaminants of Emerging Concern

et al. 2021

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Two significant limitations of using TiO2 nanoparticles for water treatment applications are reduced photocatalytic activity under visible radiation and difficulty recovering the particles after use. In this study, round-shaped Ag@TiO2 nanocomposites with a ≈21 nm diameter and a bandgap energy of 2.8 eV were synthesised by a deposition-precipitation method. These nanocomposites were immobilised into a porous poly (vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) matrix and well-distributed within the pores. The photocatalytic activity of Ag@TiO2/PVDF-HFP against metronidazole (MNZ) under solar radiation was evaluated. Further, an adaptive neuro-fuzzy inference system (ANFIS) was applied to predict the effect of four independent variables, including initial pollutant concentration, pH, light irradiation intensity, and reaction time, on the photocatalytic performance of the composite membrane on MNZ degradation. The 10% Ag@TiO2/PVDF-HFP composite membrane showed a maximum removal efficiency of 100% after 5 h under solar radiation. After three use cycles, this efficiency remained practically constant, demonstrating the membranes’ reusability and suitability for water remediation applications.

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“…Several investigations have been conducted to assess the aeration efficiency of hydraulic structures. (Dursun, 2016) and (Tiwari & Sihag, 2020) assessed the Avery and Novak (1978) E 20 = 1 + 1 1 + 0.24 * 10 −4 Fr 1.78 R 0.53 e 1.115 Markofsky and Kobus (1978) E 20 = 1 − 1 1 + 0.1Fr 1.2 1.115 Wormleaton and Tsang (2000) E 20 = 1 − [1 + 0.385 * 10 −6 Fr 2.297 R 0.684 e ] −1 Note; Fr: Froude number, Re: Reynolds number.…”

Section: Introductionmentioning

confidence: 99%

Machine learning model development for predicting aeration efficiency through Parshall flume

Sangeeta

Asadollah

Sharafati

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E 20 ) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model (RMSE testing = 0.002, R 2 testing = 0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.

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Prediction of oxygen transfer at modified Parshall flumes using regression models

Cited by 26 publications

References 36 publications

Evaluating Parshall flume aeration with experimental observations and advance soft computing techniques

Evaluating Parshall flume aeration with experimental observations and advance soft computing techniques

Reusable Ag@TiO2-Based Photocatalytic Nanocomposite Membranes for Solar Degradation of Contaminants of Emerging Concern

Machine learning model development for predicting aeration efficiency through Parshall flume

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