Prediction of local scour around complex piers using GEP and M5-Tree

Mohammadpour, Reza

doi:10.1007/s12517-017-3203-x

Cited by 20 publications

(4 citation statements)

References 34 publications

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“…In order to simplify the analysis and have simple formulas, it is convenient to group the parameters having same dimensions of Equation ( 4) in new dimensionless parameters using the Vaschy-Buckingham Theorem π as described by (Link et al 2017;Mohammadpour 2017);…”

Section: Dimensional Analysismentioning

confidence: 99%

Development of a local scour prediction model clustered by soil class

Annad

Lefkir

Mammar-kouadri

et al. 2021

Water Practice and Technology

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Several studies have been conducted to assess local scour formulas in order to select the most appropriate one. Confronted to the limits of the previous formulas, further studies have been performed to propose new local scour formulas. Generalizing a single scour formula, for all soil classes, seems approximate for such a complex phenomenon depending on several parameters and may eventually lead to considerable uncertainties in scour estimation. This study aims to propose several new scour formulas for different granulometric classes of the streambed by exploiting a large field database. The new scour formulas are based on multiple non-linear regression (MNLR) models. Supervised learning is used as an optimization tool to solve the hyper-parameters of each new equation by using the ‘Gradient Descent Algorithm’. The results show that the new formulas proposed in this study perform better than some other empirical formulas chosen for comparison. The results are presented as seven new formulas, as well as abacuses for the calculation of local scour by soil classes.

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Section: Dimensional Analysismentioning

confidence: 99%

Development of a local scour prediction model clustered by soil class

Annad

Lefkir

Mammar-kouadri

et al. 2021

Water Practice and Technology

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“…It was noted that the regression model for the reduction of pollutant was significant at a confidence level of 95% (p < 0.05) with R 2 equal to 0.86, 0.82, 0.71, 0.68 0.54, 0.54 and for BOD 5 / COD, AN, TSS, colour, BOD and COD respectively. The coefficient of determination (R 2 ) indicates that although the regression model was able to successfully predict BOD 5 /COD (R 2 =0.86) and AN (R 2 =0.82), other techniques such as soft computing methods can be recommended to prodive higher accuracy for other parameters (Zakaria et al, 2010;Mohammadpour et al, 2014;2018;2019;Ghani and Mohammadpour, 2015;Mohammadpour 2017). A comparison between actual and predicted TSS and COD removal is illustrated in Figure 9.…”

Section: P R E S Smentioning

confidence: 99%

Post-Treatment of Palm Oil Mill Effluent Using Zeolite and Wastewater

Farraji¹

2020

JOPR

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The palm oil mill effluent (POME) is one of the most important ecosystems hazard and can become a crucial environmental burden if discharged without any treatment to nature. The present study aimed to develop a fast method for post-treatment of POME. To enhance treatment process, the domestic wastewater (DWW) and zeolite were added to the sequencing batch reactor (SBR) as the available microbial source and new adsorbent, respectively. The results indicate that the chemical oxygen demand (COD), biochemical oxygen demand (BOD), total suspended solids (TSS), ammonia nitrogen (AN) and colour removal rates were in the range of 95.34%-98.31%, 88.79%-91.44%, 95.47%-98.95%, 96.19%-98.30% and 56.94%-81.64%, respectively. Moreover, SBR with both DWW and zeolite addition was able to remove high percentage of all pollution compared to only DWW addition (with a removal percentage of 77% TSS, 74% COD, 76% colour and 90% AN) or zeolite (23% TSS, 10% COD, 9.6% colour and 80% AN). The response surface methodology (RSM) was used to elucidate response surface and optimise the independent variables. The highest desirability of POME treatment (0.988) was achieved in optimum operation conditions. Under these conditions, COD, BOD, colour, AN and TSS removal rates were 96.80%, 90.1%, 69.90%, 98.20% and 97.20%, respectively.

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“…Ayoubloo et al (2015) used different methods for predicting the scour depth downstream of ski-jump spillways such as SVM, classification and regression tree (CART), and M5; it was concluded that CART can be considered the most accurate method. Mohammadpour (2017) estimated the local scour around piers using M5-Tree, Gene Expression Programming (GEP), and ANNs; he concluded that the RBF network yields better results compared with the other methods including conventional equations. Hassanzadeh et al (2019) used an optimized ANFIS with different optimization methods to estimate the scour depth in the bridge pier; the results showed that the new methods are highly efficient in the predictions.…”

Section: -Introductionmentioning

confidence: 99%

Comparative study of soft computing models for prediction of scour below two symmetric crossing jets

2022

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The scour estimation downstream of the dam's hydraulic structures is a serious issue and has long been considered an important topic by hydraulic engineers. The literature shows that the computational methods are good alternatives for the prediction of the scour in various hydraulic structures in which the conventional methods demonstrate shortcomings. In the present paper, the different machine learning techniques have been used for the first time to predict the scour below the two symmetric crossing jets. Four different artificial neural networks (ANNs), including feed forward back propagation (FFBP), cascade forward back propagation (CFBP), radial basis function (RBF), generalized regression neural network (GRNN) along with the adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR) were employed and evaluated. Two important scour hole characteristics, namely the maximum scour hole depth, and its location relative to the scour hole origin, are predicted at different crossing angles. The soft computing models are also compared with the traditional empirical methods. The results indicate that the applied computing techniques perform satisfactorily and improve the results remarkably. They can estimate the scour more precisely than the regression models and can be considered robust alternative tools. A detailed sensitivity analysis is also performed on the FFBP that shows the crossing angle parameter has a powerful effect on the predictions. The SVR improved the results to 47% and 31.71% for the scour hole depth and its location in terms of the coefficient of correlation.

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Prediction of local scour around complex piers using GEP and M5-Tree

Cited by 20 publications

References 34 publications

Development of a local scour prediction model clustered by soil class

Development of a local scour prediction model clustered by soil class

Post-Treatment of Palm Oil Mill Effluent Using Zeolite and Wastewater

Comparative study of soft computing models for prediction of scour below two symmetric crossing jets

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