Predictive modeling of discharge of flow in compound open channel using radial basis neural network

Parsaie, Abbas; Najafian, Sharareh; Shamsi, Zahra

doi:10.1007/s40808-016-0207-6

Cited by 18 publications

(5 citation statements)

References 31 publications

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“…Artificial Neural Network: It is a machine learning technique for numerical forecasting [49] , [58] . The theory of an artificial neural network (ANN) had first been developed in the field of biology, where neural networks play a crucial role in the human body.…”

Section: Elements In Aeration Efficiency As Under:-aeration Dissolved...mentioning

confidence: 99%

A review: Aeration efficiency of hydraulic structures in diffusing DO in water

Puri¹,

Sihag²,

Thakur³

2023

MethodsX

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Section: Elements In Aeration Efficiency As Under:-aeration Dissolved...mentioning

confidence: 99%

A review: Aeration efficiency of hydraulic structures in diffusing DO in water

Puri¹,

Sihag²,

Thakur³

2023

MethodsX

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“…The optimum value of MAPE for best fit simulated with regarding to the observed is zero, (Shamsi et al, 2016)]. It can be calculated according to Equation (11).…”

Section: Mean Absolute Percentage Error (Mape)mentioning

confidence: 99%

Environmental Quality Management through Parshall Flume Aeration Efficiency Modelling

Hamed¹

2023

J. Ecol. Eng.

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The dissolved oxygen content in surface waters is one of the vital indicators for human water quality usage as well as the aquatic plant and animal environmental life sustainability. Parshall flumes are one of the important ejector devices that are successfully used for oxygen requirement satisfying in various irrigation, wastewater, and ecosystems. However, the present study aimed to manage and improve various waterworks aeration efficiency through integrated modeling of experimental and analytical analysis as well as their operation conditional parameters for the Parshall flumes configuration. On the basis of the experiment work data sets run results, the principal component regression (PCR), partial least squares (PLS), and ridge regression (RR) techniques are used to develop the required aeration efficiency prediction models for such aerators by interrelating the impact of Parshall flumes characteristics and configurations, as well as various water flow rates on aeration efficiency. The predictive models developed in the study were statistically compared to the experimental data. The comparison confirms a good reliability and high accuracy. Considering the proposed aeration models, the optimum design of the new Parshall flumes can be successfully facilitated.

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“…where x represents the input variable, c is the center, and σ is the variance. Further detailed information on the basics of developing and deploying MLPNN and RBNN networks has been provided in detail in many other references [32][33][34]. In this study, the Levenberg-Marquardt (LM) learning method is applied to train the network based on its fast convergence capability for complex datasets.…”

Section: Artificial Neural Network (Mlpnn and Rbfnn)mentioning

confidence: 99%

Kernel Extreme Learning Machine: An Efficient Model for Estimating Daily Dew Point Temperature Using Weather Data

Alizamir

Kim

Zounemat‐Kermani

et al. 2020

Water

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Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water resource management. This study investigates kernel extreme learning machine (KELM), boosted regression tree (BRT), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and multivariate adaptive regression spline (MARS) models for daily dew point temperature estimation at Durham and UC Riverside stations in the United States. Daily time scale measured hydrometeorological data, including wind speed (WS), maximum air temperature (TMAX), minimum air temperature (TMIN), maximum relative humidity (RHMAX), minimum relative humidity (RHMIN), vapor pressure (VP), soil temperature (ST), solar radiation (SR), and dew point temperature (Tdew) were utilized to investigate the applied predictive models. Results of the KELM model were compared with other models using eight different input combinations with respect to root mean square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) statistical indices. Results showed that the KELM models, using three input parameters, VP, TMAX, and RHMIN, with RMSE = 0.419 °C, NSE = 0.995, and R2 = 0.995 at Durham station, and seven input parameters, VP, ST, RHMAX, TMIN, RHMIN, TMAX, and WS, with RMSE = 0.485 °C, NSE = 0.994, and R2 = 0.994 at UC Riverside station, exhibited better performance in the modeling of daily Tdew. Finally, it was concluded from a comparison of the results that out of the five models applied, the KELM model was found to be the most robust by improving the performance of BRT, RBFNN, MLPNN, and MARS models in the testing phase at both stations.

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Predictive modeling of discharge of flow in compound open channel using radial basis neural network

Cited by 18 publications

References 31 publications

A review: Aeration efficiency of hydraulic structures in diffusing DO in water

A review: Aeration efficiency of hydraulic structures in diffusing DO in water

Environmental Quality Management through Parshall Flume Aeration Efficiency Modelling

Kernel Extreme Learning Machine: An Efficient Model for Estimating Daily Dew Point Temperature Using Weather Data

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