Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System

Haghiabi, Amir Hamzeh; Parsaie, Abbas; Ememgholizadeh, Samad

doi:10.1016/j.aej.2017.05.005

Cited by 67 publications

(15 citation statements)

References 28 publications

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“…The same dataset was used for training and testing of MLPNN. The MLPNN which was proposed by Haghiabi et al (2018) was considered. They recommend that, in order to reduce the trial and error process in designing the structure of the MLPNN, first, a single-layer network, which contains a number of neurons equal to the number of input features, is considered.…”

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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“…The MAE and RMSE indices in Tables 2 and 3 only show the average error in model operation and do not give any information about the error distribution. To overcome this problem we propose to use the developed discrepancy ratio (DDR) index, proposed in the literature for evaluating prediction models; see, e.g., [22][23][24][25].…”

Section: Current Virtual Sensormentioning

confidence: 99%

A Virtual Sensor for Electric Vehicles’ State of Charge Estimation

et al. 2020

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The estimation of the state of charge is a critical function in the operation of electric vehicles. The battery management system must provide accurate information about the battery state, even in the presence of failures in the vehicle sensors. This article presents a new methodology for the state of charge estimation (SOC) in electric vehicles without the use of a battery current sensor, relying on a virtual sensor, based on other available vehicle measurements, such as speed, battery voltage and acceleration pedal position. The estimator was derived from experimental data, employing support vector regression (SVR), principal component analysis (PCA) and a dual polarization (DP) battery model (BM). It is shown that the obtained model is able to predict the state of charge of the battery with acceptable precision in the case of a failure of the current sensor.

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“…Artificial neural network (ANN) is the most common computing technique which is based on the nerve cells of the human brains. ANN is successfully used in hydrological and water resources problems (Sihag 2018;Sihag et al 2018d;Haghiabi et al 2017b;Sihag et al 2017c;Parsaie et al 2016b;Haghiabi 2014a, 2015b). Neurons are arranged in the form of layers.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Evaluation of the impact of fly ash on infiltration characteristics using different soft computing techniques

et al. 2018

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The aim of this paper was to investigate the impact of the fly ash concentration on the infiltration process and to assess the potential of five soft computing techniques such as artificial neural network, Gaussian process, support vector machine (SVM), random forest, and M5P model tree and compare with two popular conventional models, SCS and Kostiakov mode, to estimate the cumulative infiltration of fly-ash-mixed soils. Laboratory experiment was carried out with the different combinations of the sand, clay, and fly ash by using mini disk infiltrometer. The combination consists of the different concentrations of sand (25-45%), clay (25-45%), and fly ash (10-50%). The total observation consists of the 138 field measurement. The cumulative infiltration increase with an increment in the concentration of the fly ash, but it decreases when fly ash concentration increases 40-50% in the soil. On the other hand, the cumulative infiltration increases with the decrease in the concentration of clay in samples. The predictive modeling technique, SVM with RBF kernel, is the best technique to predict the cumulative infiltration with minimum error. Results suggest that SVM with RBF kernel is the best-fit modeling technique among other soft computing techniques as well as conventional models to find the impact of fly ash on infiltration characteristics for the given combination of the sand, clay and fly ash.

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Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System

Cited by 67 publications

References 28 publications

Evaluation of energy dissipation on stepped spillway using evolutionary computing

Evaluation of energy dissipation on stepped spillway using evolutionary computing

A Virtual Sensor for Electric Vehicles’ State of Charge Estimation

Evaluation of the impact of fly ash on infiltration characteristics using different soft computing techniques

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