Prediction of Energy Dissipation of Flow Over Stepped Spillways Using Data-Driven Models

Parsaie, Abbas; Haghiabi, Amir Hamzeh; Saneie, Mojtaba; Torabi, Hasan

doi:10.1007/s40996-017-0060-5

Cited by 32 publications

(11 citation statements)

References 56 publications

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“…Comparing the performance of M5 algorithm with MLPNN shows that the accuracy of M5 algorithm is a bit more than MLPNN. The performance of stepped spillways regarding energy dissipation has been predicted using group method of data handling (GMDH), genetic programming (GP), support vector machine (SVM) and multivariate adaptive regression splines (MARS) (Parsaie et al 2016b(Parsaie et al , 2018a. According to the reports, the error indices of MARS technique in preparation stages were R 2 = 0.99 and RMSE = 0.65.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of energy dissipation on stepped spillway using evolutionary computing

Parsaie

Haghiabi

2019

Appl Water Sci

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In this study, using the M5 algorithm and multilayer perceptron neural network (MLPNN), the capability of stepped spillways regarding energy dissipation (ED) was approximated. For this purpose, relevant data was collected from valid sources. The study of the developed model based on the M5 algorithm showed that the Drop and Froude numbers play important roles in modeling and approximating the ED. The error indices of M5 algorithm in training were R 2 = 0.99 and RMSE = 2.48 and in testing were R 2 = 0.99 and RMSE = 2.23. The study of developed MLPNN revealed that this model has one hidden layer which includes five neurons. Among the tested transfer functions, the great efficiency was related to the Tansing function. The error indices of MLPNN in training were R 2 = 0.97 and RMSE = 3.73 and in testing stages were R 2 = 0.97 and RMSE = 3.98. Evaluation of the results of both applied methods shows that the accuracy of the MLPNN is a bit less than the M5 algorithm. Keywords Energy dissipation • Soft computing • Drop number • Spillways • M5 algorithm Abbreviations ANFIS Adaptive neuro-fuzzy inference system CFD Computational fluid dynamic DN Drop number ED Energy dissipation Fr Froude number g Gravity acceleration GEP Genetic expression programming GMDH Group method of data handling H Total head of flow h s Height of steps L c Length of crest L s Length of steps MARS Multivariate adaptive regression splines Max Maximum Min Minimum MLPNN Multilayer perceptron neural network N Number of steps q Discharge per width y 0 Flow depth over the crest y 1 and y 2 Conjugated depths of hydraulic jump This article is part of the Topical Collection "Water and Energy" guest edited by Enrico Drioli.

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Section: Resultsmentioning

confidence: 99%

Evaluation of energy dissipation on stepped spillway using evolutionary computing

Parsaie

Haghiabi

2019

Appl Water Sci

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“…Hence, researchers investigate other fast, easy, and accurate methods for scouring estimation in hydraulic studies. Recently, hydraulic experts employ soft computing techniques in estimating scouring in many studies (Muzzammil 2008;Guven et al 2009;Adarsh 2010;Ebtehaj and Bonakdari 2013;Rikar et al 2016;Najafzadeh et al 2017;Parsaie et al 2018;Abdollahpour et al 2019). The main goal of prediction with AI techniques was following the recent developments to obtain the best model performances (Khatibi et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Hybrid model of support vector regression and innovative gunner optimization algorithm for estimating ski-jump spillway scour depth

et al. 2022

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Scour hole that occurs downstream of the hydraulic structures threatens the safety and stability of the hydraulic structures. The scour around the structures is a complex and important hydraulic phenomenon; hence, it requires a data extensive research for the accurate estimation of scour depth. Although many analytical models are available for scour depth estimation, they suffer from huge limitations. In this research, the support vector regression (SVR) model and SVR ensemble with the metaheuristic algorithm of innovative gunner (SVR-AIG) models have been developed for accurate prediction of scour depth downstream of the ski-jump spillways. Field measurements including head and discharge intensity are used for developing the models. The performances of the models are compared using root mean square error (RMSE), mean average error (MAE), and correlation coefficient (CC) criteria and some statistical plots. The results showed that the hybrid SVR-AIG-based estimations (with CC = 0.987, 0.991, RMSE = 2.839, 1.987, and MAE = 2.247, 1.201) are more accurate than the SVR standalone model estimations (with CC = 0.942, 0.975, RMSE = 5.686, 4.040, and MAE = 4.114, 3.201) at the training and testing phases. This study is an important reference for analyzing the high capability of the AIG as an optimization tool in improving scour estimations of a standalone model. Also, this algorithm eliminates the trial-and-error procedure to optimize the internal parameters during the model development. Graphical abstract

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“…In the last few years, Artificial Intelligence (AI) techniques have been widely applied in different fields of water resources as they are capable of resolving intricate problems that did not have a tractable solution (Aghelpour et al 2019;Jahani & Mohammadi 2019;Sihag et al 2019bSihag et al , 2019cThakur et al 2021). AIbased models/algorithms have been employed extensively in many water resources applications, including hydrology (Banadkooki et al 2020;Malik et al 2020b;Tikhamarine et al 2020c;Ghasempour et al 2021;Sihag et al 2021), hydraulics (Parsaie 2016;Parsaie et al 2016Parsaie et al , 2018Ebtehaj et al 2017;Najafzadeh et al 2017;Sihag et al 2019a), and water flow/quality (Heddam & Kisi 2017;Parsaie & Haghiabi 2017aHaghiabi et al 2018;Singh et al 2019;Esmaeilbeiki et al 2020;Pandhiani et al 2020). Nevertheless, limited numbers of studies have considered the application of AI for the evaluation of Parshall flume aeration performance.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Prediction of Energy Dissipation of Flow Over Stepped Spillways Using Data-Driven Models

Cited by 32 publications

References 56 publications

Evaluation of energy dissipation on stepped spillway using evolutionary computing

Evaluation of energy dissipation on stepped spillway using evolutionary computing

Hybrid model of support vector regression and innovative gunner optimization algorithm for estimating ski-jump spillway scour depth

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

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