scholarly journals An evaluation of ANN methods for estimating the lengths of hydraulic jumps in U-shaped channel

2012 ◽  
Vol 15 (1) ◽  
pp. 147-154 ◽  
Author(s):  
Larbi Houichi ◽  
Noureddine Dechemi ◽  
Salim Heddam ◽  
Bachir Achour
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hydraulic Jump ◽  
Empirical Models ◽  
Generalized Regression Neural Network ◽  
Hydraulic Jumps ◽  
Hydraulic Characteristics ◽  
Stilling Basins ◽  
High Turbulence ◽  
Predictive Strength

Modelling of hydraulic characteristics of jump using theoretical and empirical models has always been a difficult task. The length of jump may be defined as the distance measured from the toe of the jump to the location of the surface rise. Due to high turbulence this length cannot be determined easily by theory. However, it has been investigated experimentally so as to design the stilling basins with hydraulic jumps. In this work, the control of a hydraulic jump by broad-crested sills in a U-shaped channel is recalled theoretically and experimentally examined. The study begins with a multiple regression (MR) analysis. Then, and in order to model the relative lengths of hydraulic jumps, we have implemented and evaluated two different artificial neural networks (ANN): multilayer perceptron neural network (MLPNN) and generalized regression neural network (GRNN). The results demonstrate the predictive strength of GRNN and its potential to predict hydraulic problems with an adaptive spread value. However, the MLPNN model remains best classified by these indexes of performance.

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

scholarly journals Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics

2019 ◽  
pp. 77-86
Author(s):  
A. G. Buevich ◽  
I. E. Subbotina ◽  
A. V. Shichkin ◽  
A. P. Sergeev ◽  
E. M. Baglaeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Surface Soil ◽  
Soil Layer ◽  
Generalized Regression Neural Network ◽  
Residual Kriging ◽  
Surface Soil Layer ◽  
Artificial Neural ◽  
Generalized Regression

Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screening of the surface soil layer in the subarctic Noyabrsk, Russia. The models are developed based on computer modeling with minimization of the RMSE. The MLPRK model showed the best prognostic accuracy.

Heart Disease Classification Using Artificial Neural Networks

2015 ◽  
Vol 781 ◽  
pp. 624-627 ◽  
Author(s):  
Rati Wongsathan ◽  
Pasit Pothong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Heart Disease ◽  
Classification Accuracy ◽  
High Performance ◽  
Disease Classification ◽  
Generalized Regression Neural Network ◽  
The Neural Networks ◽  
Very High

Neural Networks (NNs) has emerged as an importance tool for classification in the field of decision making. The main objective of this work is to design the structure and select the optimized parameter in the neural networks to implement the heart disease classifier. Three types of neural networks, i.e. Multi-layered Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), and Generalized Regression Neural Network (GR-NN) have been used to test the performance of heart disease classification. The classification accuracy obtained by RBFNN gave a very high performance than MLP-NN and GR-NN respectively. The performance of accuracy is very promising compared with the previously reported another type of neural networks.

Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers

2015 ◽  
Author(s):  
Ehsan Sarshari ◽  
Philippe Mullhaupt
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Empirical Models ◽  
Local Scour ◽  
Bridge Piers ◽  
Local Conditions ◽  
The Neural Network ◽  
Artificial Neural

Scour can have the effect of subsidence of the piers in bridges, which can ultimately lead to the total collapse of these systems. Effective bridge design needs appropriate information on the equilibrium depth of local scour. The flow field around bridge piers is complex so that deriving a theoretical model for predicting the exact equilibrium depth of local scour seems to be near impossible. On the other hand, the assessment of empirical models highly depends on local conditions, which is usually too conservative. In the present study, artificial neural networks are used to estimate the equilibrium depth of the local scour around bridge piers. Assuming such equilibrium depth is a function of five variables, and using experimental data, a neural network model is trained to predict this equilibrium depth. Multilayer neural networks with backpropagation algorithm with different learning rules are investigated and implemented. Different methods of data normalization besides the effect of initial weightings and overtraining phenomenon are addressed. The results show well adoption of the neural network predictions against experimental data in comparison with the estimation of empirical models.

Prediction of pressure fluctuations on sloping stilling basins

2006 ◽  
Vol 33 (11) ◽  
pp. 1379-1388 ◽  
Author(s):  
A Güven ◽  
M Günal ◽  
A Çevik
Keyword(s):  
Neural Network ◽  
Pressure Fluctuation ◽  
Hydraulic Jump ◽  
Regression Models ◽  
Pressure Fluctuations ◽  
Network Models ◽  
Neural Network Models ◽  
Mean Pressure ◽  
Stilling Basins ◽  
The Mean

Various types of hydraulic jump occurring on horizontal and sloping channels have been analyzed experimentally, theoretically, and numerically and the results are available in the literature. In this study, artificial neural network models were developed to simulate the mean pressure fluctuations beneath a hydraulic jump occurring on sloping stilling basins. Multilayers feed a forward neural network with a back-propagation learning algorithm to model the pressure fluctuations beneath such a type of hydraulic jump (B-jump). An explicit formula that predicts the mean pressure fluctuation in terms of the characteristics that contribute most to the hydraulic jump occurring on the sloping basins is presented. The proposed neural network models are compared with linear and nonlinear regression models that were developed using considered physical parameters. The results of the neural network modelling are found to be superior to the regression models and are in good agreement with the experimental results due to relatively small values of error (mean absolute percentage error).Key words: neural networks, pressure fluctuation, hydraulic jump, sloping stilling basin, explicit NN formulation, regression analysis.

Detection of Organic Pollutions Using Artificial Neural Networks and Domain Transform Techniques

2014 ◽  
Vol 610 ◽  
pp. 279-282
Author(s):  
Ling Gao ◽  
Shou Xin Ren
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Noise Removal ◽  
Generalized Regression Neural Network ◽  
Mathematical Methods ◽  
Artificial Neural ◽  
Generalized Regression

This paper presented a novel method for detection of organic pollutions based on artificial neural networks combining domain transform techniques. Domain transform techniques are mathematical methods that allow the direct mapping of information from one domain to another. The most effectively used domain transform technique is wavelet packet transform (WPT). Wavelet packet representations of signals provided a local timefrequency description and separation ability between information and noise. The quality of the noise removal can be further improved by using best-basis algorithm and thresholding operation. Artificial neural network (ANN) is a form of artificial intelligence that mathematically simulates biological nervous system. Generalized regression neural network (GRNN) is a kind of ANN and is applied for overcoming the convergence problem met in back propagation training and facilitating nonlinear calculation. In the case a method named WPT-based generalized regression neural network (WPTGRNN) was used for analyzing overlapping spectra.

Radial flow stilling basins with baffle blocks

1989 ◽  
Vol 16 (4) ◽  
pp. 489-497 ◽  
Author(s):  
Peter C. Nettleton ◽  
John A. McCorquodale
Keyword(s):  
Key Words ◽  
Hydraulic Jump ◽  
Water Surface ◽  
Radial Flow ◽  
Surface Profile ◽  
Hydraulic Jumps ◽  
Bureau Of Reclamation ◽  
Stilling Basin ◽  
Jump Length ◽  
Stilling Basins

A total of 120 tests of forced radial flow hydraulic jumps have been analyzed in order to develop curves and equations for the design of radial stilling basins. The jump depth, the water surface profile, wave amplitudes, the allowable flare angle, and the jump length are defined in terms of entrance conditions, the baffle position, and the baffle height. An example design is given and compared with a USBR (U.S. Bureau of Reclamation) Type III stilling basin. Key words: forced hydraulic jump, radial flow, design, stilling basins, baffles, radial hydraulic jump, circular hydraulic jump.

scholarly journals Pressure Fluctuations in the Spatial Hydraulic Jump in Stilling Basins with Different Expansion Ratio

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Nasrin Hassanpour ◽  
Ali Hosseinzadeh Dalir ◽  
Arnau Bayon ◽  
Milad Abdollahpour
Keyword(s):  
Hydraulic Jump ◽  
Laboratory Tests ◽  
Pressure Fluctuations ◽  
Expansion Ratio ◽  
Hydraulic Jumps ◽  
Extreme Pressure ◽  
Flow Conditions ◽  
Stilling Basins ◽  
Channel Expansion ◽  
Shed Light

Pressure fluctuations are a key issue in hydraulic engineering. However, despite the large number of studies on the topic, their role in spatial hydraulic jumps is not yet fully understood. The results herein shed light on the formation of eddies and the derived pressure fluctuations in stilling basins with different expansion ratios. Laboratory tests are conducted in a horizontal rectangular flume with 0.5 m width and 10 m length. The range of approaching Froude numbers spans from 6.4 to 12.5 and the channel expansion ratios are 0.4, 0.6, 0.8, and 1. The effects of approaching flow conditions and expansion ratios are thoroughly analyzed, focusing on the dimensionless standard deviation of pressure fluctuations and extreme pressure fluctuations. The results reveal that these variables show a clear dependence on the Froude number and the distance to the hydraulic jump toe. The maximum values of extreme pressure fluctuations occur in the range 0.609<X<3.385, where X is dimensionless distance from the toe of the hydraulic jump, which makes it highly advisable to reinforce the bed of stilling basins within this range.

scholarly journals Peramalan Kunjungan Wisatawan Mancanegara Menggunakan Generalized Regression Neural Networks

2016 ◽  
Vol 8 (1) ◽  
pp. 35
Author(s):  
Sri Herawati
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Generalized Regression Neural Network ◽  
Generalized Regression Neural Networks ◽  
Generalized Regression

Peramalan kunjungan wisatawan mancanegara (wisman) sangat penting bagi pemerintah dan industri, karena peramalan menjadi dasar dalam perencanaan kebijakan yang efektif. Penelitian ini menggunakan Generalized Regression Neural Network (GRNN) untuk meramalkan kunjungan wisman menurut 19 pintu masuk utama dan kebangsaan, seperti: Ngurah Rai, Soekarno-Hatta, Batam, Tanjung Uban, Polonia, Juanda, Husein Sastranegara, Tanjung Balai Karimun, Tanjung Pinang, Tanjung Priok, Adi Sucipto, Minangkabau, Entikong, Adi Sumarmo, Sultan Syarif Kasim II, Sepinggan, Sam Ratulangi, Bandara Internasional Lombok, dan Makassar. GRNN memiliki kelebihan tidak memerlukan estimasi jumlah bobot jaringan untuk mendapatkan arsitektur jaringan optimal, sehingga tidak memerlukan pengaturan parameter bebas. Uji coba penelitian dilakukan dengan menggunakan spread dari 0,1 sampai 1,0. Hasil uji coba menunjukkan bahwa kinerja Peramalan terbaik dengan menggunakan spread 0,1 baik untuk data latih maupun data uji

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