Applications of Radial-Basis Function and Generalized Regression Neural Networks for Modeling of Coagulant Dosage in a Drinking Water-Treatment Plant: Comparative Study

Heddam, Salim; Bermad, Abdelmalek; Dechemi, Noureddine

doi:10.1061/(asce)ee.1943-7870.0000435

Cited by 54 publications

(17 citation statements)

References 20 publications

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“…Through the training of the GRNN with the CPFM, which was derived from the measured frequencies and modal shape components, with the input and the joint element stiffness reduction factors as the output, the joint element reduction factor of the test shell could be identified by the reverse neural network model. For further information and a complete formulation of GRNN as well as their detailed implementations steps, the reader is referred to a number of publications in the literature, such as [1,[15][16][17][18]. The initial correction results of the stiffness reduction factor were drawn ( 11 = 0.721, 12 = 0.573, and 13 = 0.201).…”

Section: Analysis Of Corrected Results According Tomentioning

confidence: 99%

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Finite Element Model Fractional Steps Updating Strategy for Spatial Lattice Structures Based on Generalized Regression Neural Network

Liu

Miao

Zhao

2016

Shock and Vibration

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In order to get a more accurate finite element model of a spatial lattice structure with bolt-ball joints for health monitoring, a method of modifying the bolt-ball joint stiffness coefficient was proposed. Firstly, the beam element with adjustable stiffness was used in the joint zone in this paper to reveal the semirigid characteristic of the joint. Secondly, the value of stiffness reduction factor (ar) was limited in the range of[0.2,0.8]and the reference value (ar0) of it was suggested to be 0.5 based on referenced literatures. Finally, the finite element model fractional steps updating strategy based on neural network technique was applied and the limited measuring point information was used to form the network input parameter. A single-layer latticed cylindrical shell model with 157 joints and 414 tubes was used in a shaking TABLE test. Based on the measured modal data, the presented method was verified. The results show that this model updating technique can reflect the true dynamic characters of the shell structure better. Moreover, the neural network can be simplified considerably by using this algorithm. The method can be used for model updating of a latticed shell with bolt-ball joints and has great value in engineering practice.

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Section: Analysis Of Corrected Results According Tomentioning

confidence: 99%

“…Furthermore, they differ from classical neural networks in that every weight is replaced by a distribution of weights. GRNN is related to the RBF, based on a standard statistical technique called Gaussian kernel regression [18].…”

Section: Updating Strategy Basing On Grnnmentioning

confidence: 99%

Finite Element Model Fractional Steps Updating Strategy for Spatial Lattice Structures Based on Generalized Regression Neural Network

Liu

Miao

Zhao

2016

Shock and Vibration

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“…[23] Due to its good performance, the GRNN has been extensively used in various prediction and forecasting tasks in civil engineering and hydrologic modelling. For example, Heddam et al [25] developed a GRNN model for coagulant dosage modelling in Algeria. Fahmy and Moselhi [26] presented a model designed to forecast the remaining useful life of cast iron water mains in Canada and the USA, based on the GRNN neural network.…”

Section: Grnn-based Modelmentioning

confidence: 99%

Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA

Heddam

2014

Environmental Technology

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In this study, a comparison between generalized regression neural network (GRNN) and multiple linear regression (MLR) models is given on the effectiveness of modelling dissolved oxygen (DO) concentration in a river. The two models are developed using hourly experimental data collected from the United States Geological Survey (USGS Station No: 421209121463000 [top]) station at the Klamath River at Railroad Bridge at Lake Ewauna. The input variables used for the two models are water, pH, temperature, electrical conductivity, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), the mean absolute error (MAE), Willmott's index of agreement (d), and correlation coefficient (CC) statistics. Of the two approaches employed, the best fit was obtained using the GRNN model with the four input variables used.

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“…m k and S dk are the mean value and standard deviation of the variable k (input or output). Normalization is an important process, which increases significantly the performance of the models (Kingston et al 2005;Heddam et al 2011Heddam et al , 2012Heddam et al , 2016Heddam 2016aHeddam , 2016b.…”

Section: Introductionmentioning

confidence: 99%

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

Heddam

2016

Environ. Process.

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This study presents a new method called optimally pruned extreme learning machine (OP-ELM) for forecasting dissolved oxygen concentration (DO) several hours in advance. The forecast time horizon ranges from 24-h ahead (one day) to 168-h ahead (seven days). The proposed OP-ELM model is compared to the standard multilayer perceptron neural network (MLPNN) with respect to their capabilities of forecasting DO in the Klamath River at Miller Island Boat Ramp, Oregon, USA. To demonstrate the forecasting capability of OP-ELM and MLPNN models, we used a long-term data set of hourly DO data for a ten-year period, from 1 January 2004 to 31 December 2013, collected by the United States Geological Survey (USGS Stations No: 420,853,121,505,500 [Top] and 420,853,121,505,501 [Bottom]). For developing the models, we split the data set into a training subset (from 2004 to 2010) that corresponded to 70 %, and a validation (from 2011 to 2013) that corresponded to 30 % of the total data set. We investigated the performance and accuracy of the proposed two models for three different horizons, i.e., short-term, medium-term and long-term forecasting; a total of six different models (FM1 to FM6), having the same data sets as inputs, were developed for short-term (24 h to 48 h), medium-term (72 h to 96 h) and long-term (120 h to 168 h) horizons. Input variables used in the six models were the six antecedent DO concentrations at (t-5), (t-4), (t-3), (t-2), (t-1) and (t). The performance of the OP-ELM and MLPNN models in training and validation sets were compared with the observed data. To get more accurate evaluation of the results of the two models, the following seven statistical performance indices were used: the coefficient of correlation (R), the Willmott index of agreement (d), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), the mean absolute error (MAE), the bias error (Bias), and the mean absolute percentage error (MAPE). The study reveals that OP-ELM and MLPN provided good results and they were successful in forecasting DO at a high level of accuracy. The reliability of forecasting decreased Environ. Process. with increasing the step ahead. The measures of model performance fell within the acceptable ranges for the two stations. Regarding the fact that researches on medium and long-term forecasting are relatively limited, the present work aims to build and provide a good early warning system capable of preventing DO depletion and the associated problems of anoxia and hypoxia in river. Furthermore, the proposed forecasting models, when implemented appropriately, could be reliably used in detecting future change in DO concentration in rivers.

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Applications of Radial-Basis Function and Generalized Regression Neural Networks for Modeling of Coagulant Dosage in a Drinking Water-Treatment Plant: Comparative Study

Cited by 54 publications

References 20 publications

Finite Element Model Fractional Steps Updating Strategy for Spatial Lattice Structures Based on Generalized Regression Neural Network

Finite Element Model Fractional Steps Updating Strategy for Spatial Lattice Structures Based on Generalized Regression Neural Network

Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

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