Prediction of side weir discharge coefficient by support vector machine technique

Azamathulla, Hazi Mohammad; Haghiabi, Amir Hamzeh; Parsaie, Abbas

doi:10.2166/ws.2016.014

Cited by 101 publications

(39 citation statements)

References 30 publications

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“…Tiwari et al (2017) used the generalised regression neural network, MLR, M5P model tree and SVM to predict the cumulative infiltration of soil and found that SVM works well than the other techniques. Various researchers have been used various soft computing techniques in hydraulics and environmental engineering applications (Sihag et al 2017b(Sihag et al , c, 2018aHaghiabi et al 2018;Nain et al 2018a;Tiwari et al 2018;Parsaie et al 2017a, b;Shiri et al 2016Shiri et al , 2017Parsaie andHaghiabi 2015, 2017;Parsaie 2016;Azamathulla et al 2016;Baba et al, 2013). These researchers found that these techniques work exceptionally well.…”

Section: Introductionmentioning

confidence: 99%

Modelling of the impact of water quality on the infiltration rate of the soil

2019

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The concept behind of this paper is to check the potential of the three regression-based techniques, i.e. M5P tree, support vector machine (SVM) and Gaussian process (GP), to estimate the infiltration rate of the soil and to compare with two empirical models, i.e. Kostiakov model and multi-linear regression (MLR). Totally, 132 observations were obtained from the laboratory experiments, out of which 92 observations were used for training and residual 40 for testing the models. A double-ring infiltrometer was used for experimentation with different concentrations (1%, 5%, 10% and 15%) of impurities and different types of water quality (ash and organic manure). Cumulative time (T f ), type of impurities (I t ), concentration of impurities (C i ) and moisture content (W c ) were the input variables, whereas infiltration rate was considered as target. For SVM and GP regression, two kernel functions (radial based kernel and Pearson VII kernel function) were used. The results from this investigation suggest that M5P tree technique is more precise as compared to the GP, SVR, MLR approach and Kostiakov model. Among GP, SVR, MLR approach and Kostiakov model, MLR is more accurate for estimating the infiltration rate of the soil. Thus, M5P tree is a technique which could be used for modelling the infiltration rate for the given data set. Sensitivity analyses suggest that the cumulative time (T f ) is the major influencing parameter on which infiltration rate of the soil depends.

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Section: Introductionmentioning

confidence: 99%

Modelling of the impact of water quality on the infiltration rate of the soil

2019

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“…In many applications, a nonlinear classifier provides better accuracy. In SVM, the input x is first mapped onto an m ‐dimensional feature space using some fixed (nonlinear) mapping, and then a linear model is constructed in this feature space (Azamathulla et al ., ). The naive way of making a nonlinear classifier out of a linear classifier is to map our data from the input space X to a feature space F using a nonlinear function, φ : x → f φ : x → f.…”

Section: Methodsmentioning

confidence: 97%

Improving Modelling of Discharge Coefficient of Triangular Labyrinth Lateral Weirs Using SVM, GMDH and MARS Techniques

Parsaie

Haghiabi

2017

Irrigation and Drainage

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In this study the discharge coefficient (Cd) of labyrinth lateral weirs was modelled and predicted using artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM), group method of data handling (GMDH), and multivariate adaptive regression splines (MARS) techniques. To this end, a related data set was collected from the literature. Results indicated that the proposed ANN model includes two hidden layers with eight and five neurons in the first and second hidden layers, respectively. Testing transfer functions demonstrated that the radial basis function (RBF) has the best outcome. Evaluating the performance of the ANN model shows that this model with a coefficient of determination (R2 = 0.96) and root mean square error (RMSE =0.07) in the testing stage has suitable functionality. Reviewing the ANFIS model showed that this model with one hidden layer containing seven membership functions within the Gaussian function could achieve R2 = 0.94 and RMSE =0.136 for predicting the discharge coefficient. During preparation of SVM, it was found that this model with RBF as the best kernel function obtained R2 = 0.96 and RMSE =0.07. The structure of the MARS and GMDH models pointed out that the Froude number, grandiose index and ratio of upstream flow depth to the height of weir are the most effective parameters for predicting the discharge coefficient. Minimum R2 and RMSE achieved were equal to 0.93 and 0.08, respectively, in the testing stage for the MARS and GMDH models. Copyright © 2017 John Wiley & Sons, Ltd.

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“…Due to high cost of experiments and de ciency in laboratory studies due to simpli cations and limit range of measured parameters, researchers attempt to use the mathematical approaches for modeling and predicting the scour depth at downstream of ip buckets. In the eld of mathematical modeling, using both of CFD and soft computing techniques was reported by Xiao et al [11].Nowadays, by advancing the soft computing techniques in the most areas related to hydraulic engineering, investigators have tried to use these techniques for predicting the scouring phenomena [12][13][14][15][16][17][18][19], speci cally scour depth at downstream of ip bucket. In this regard, using the Arti cial Neural Networks (ANNs), Genetic Programming (GP), Support Vector machine and M5 Model Tree, Group Method of Data Handling (GMDH), and Adaptive Neuro Fuzzy Inference System (ANFIS) can be mentioned [20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Estimation of scour downstream of a ski-jump bucket using the multivariate adaptive regression splines

Haghiabi

2017

Scientia Iranica

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Abstract. In this paper, modeling the scour downstream of a ip bucket of spillways was considered using empirical formulas, soft computing techniques such as multilayer perceptron (MLP) neural network, and Multivariate Adaptive Regression Splines (MARS). For this purpose, 95 data sets were collected with regard to the most a ective parameters on the scouring phenomena at downstream of spillways. During the MLP model development, it was found that the two transfer functions, such as log-sigmoid and radial basis, had very suitable performances for predicting the desired scouring phenomena. The results of MARS model showed that this model with coe cient of determination 0.99 and 0.91 during the development and testing stages, respectively, had suitable performance for modeling the scouring depth at downstream of ip bucket structure. The results of gamma test and MARS model indicated that q=(gd 3 w ), R=d w , and H=d w were the most a ective parameters on the scouring phenomena.

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Prediction of side weir discharge coefficient by support vector machine technique

Cited by 101 publications

References 30 publications

Modelling of the impact of water quality on the infiltration rate of the soil

Modelling of the impact of water quality on the infiltration rate of the soil

Improving Modelling of Discharge Coefficient of Triangular Labyrinth Lateral Weirs Using SVM, GMDH and MARS Techniques

Estimation of scour downstream of a ski-jump bucket using the multivariate adaptive regression splines

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