Estimation of Tunnel Desilter Sediment Removal Efficiency by ANFIS

Tiwari, N. K.; Sihag, Parveen; Singh, Bipin Kumar; Ranjan, Subodh; Singh, Krishna Kumar

doi:10.1007/s40996-019-00261-3

Cited by 10 publications

(9 citation statements)

References 28 publications

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“…These types of networks are now introduced into the multilayered perceptron neural network (MLPNN). Investigating the structure of the neuron shows that the information is firstly multiplied in a coefficient and then are summarized, and finally, its result is (Sihag et al 2019;Tiwari et al 2019).…”

Section: Review On Annsmentioning

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: Review On Annsmentioning

confidence: 99%

Evaluation of energy dissipation on stepped spillway using evolutionary computing

Parsaie

Haghiabi

2019

Appl Water Sci

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“…(Chanson, 2002) calibrated the gas-liquid interface through a stepped channel. The application of soft computing models has been drastically increased in different engineering fields (Sihag and Vajesnayee, 2018;Kumar et al, 2018Kumar et al, , 2020Sihag et al, 2017Sihag et al, , 2019Tiwari et al, 2019). Very few studies have been conducted to assess the aeration mechanism in flumes (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…(Dursun, 2016) provided an experimental laboratory dataset to assess the aeration of the small. (Tiwari et al, 2019) examined the potential of soft computing models, including adaptive neuro-fuzzy inference system (ANFIS), fuzzy logic (FL), and artificial neural network (ANN), to simulate the transfer efficiency of oxygen through the Parshall flumes.…”

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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“…To overcome these drawbacks, the current trend is to integrate these tools into hybrid architectures to take advantage of the fuzzy logic and neural networks. The use of a fuzzy neural network offers the possibility of modeling a priori knowledge and linguistic decision rules obtained by experts in the field [20][21][22][23][24][25]. Various studies show that the ANFIS Neuro-fuzzy system, known as adaptive networks based on fuzzy inference, is able to quickly learn the behavior of a system with precision, and is even better than the other methods.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy Sliding Mode Controller Based on a Brushless Doubly Fed Induction Generator

2020

IJE

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The combination of neural networks and fuzzy controllers is considered as the most efficient approach for different functions approximation, and indicates their ability to control nonlinear dynamical systems. This paper presents a hybrid control strategy called Neuro-Fuzzy Sliding Mode Control (NFSMC) based on the Brushless Doubly fed Induction Generator (BDFIG). This replaces the sliding surface of the control to exclude chattering phenomenon caused by the discontinuous control action. This technique offers attractive features, such as robustness to parameter variations. Simulations results of 2.5 KW BDFIG have been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC) and sliding mode control (SMC). We compare the static and dynamic characteristics of the three control techniques under the same operating conditions and in the same simulation configuration. The proposed controller schemes (NFSMC) are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effects of parametric uncertainties not affecting system performance.

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Estimation of Tunnel Desilter Sediment Removal Efficiency by ANFIS

Cited by 10 publications

References 28 publications

Evaluation of energy dissipation on stepped spillway using evolutionary computing

Evaluation of energy dissipation on stepped spillway using evolutionary computing

Machine learning model development for predicting aeration efficiency through Parshall flume

Neuro-fuzzy Sliding Mode Controller Based on a Brushless Doubly Fed Induction Generator

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