Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

Fırat, Mahmut; Güngör, Mahmud

doi:10.1016/j.advengsoft.2008.12.001

Cited by 159 publications

(51 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When Firat et al [42] predicted the scour depth around circular bridge piers based on the data from various studies, the GRNN model performed superior to BPNN and MLR. Among different types of ANNs, BPNN often shows a fair performance because the best model could be obtained by iterant parameters adjustment after calibration in the models [43].…”

Section: Comparison Of Results Obtained By Modelsmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

View full text Add to dashboard Cite

Freshwater reservoirs are considered as the source of atmospheric greenhouse gas (GHG), but more than 96% of global reservoirs have never been monitored. Compared to the difficulty and high cost of field measurements, statistical models are a better choice to simulate the carbon emissions from reservoirs. In this study, two types of Artificial Neural Networks (ANNs), Back Propagation Neural Network (BPNN) and Generalized Regression Neural Network (GRNN), were used to predict carbon dioxide (CO 2 ) flux emissions from reservoirs based on the published data. Input variables, which were latitude, age, the potential net primary productivity, and mean depth, were selected by Spearman correlation analysis, and then the rationality of these inputs was proved by sensitivity analysis. Besides this, a Multiple Non-Linear Regression (MNLR) and a Multiple Linear Regression (MLR) were used for comparison with ANNs. The performance of models was assessed by statistical metrics both in training and testing phases. The results indicated that ANNs gave more accurate results than regression models and GRNN provided the best performance. With the help of this GRNN, the total CO 2 emitted by global reservoirs was estimated and possible CO 2 flux emissions from a planned reservoir was assessed, which illustrated the potential application of GRNN.

show abstract

Section: Comparison Of Results Obtained By Modelsmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

View full text Add to dashboard Cite

show abstract

“…It is noted that the reliability of MLP, GRNN and RBFNN models depend on data structure used in training and testing processes and model structure. In ANN predicting models, model performance is influenced by input structure and many training parameters selected by trial and error method (Firat and Gungor 2009). Therefore, these parameters should be carefully selected.…”

Section: Resultsmentioning

confidence: 99%

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Barzegar

Moghaddam

2016

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

Salinity is one of the main factors in groundwater quality monitoring. The main objectives of this study are to investigate and compare the accuracy of three different neural computing techniques, multi-layer perceptron neural network (MLP), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN), in prediction of groundwater salinity of the Tabriz plain confined aquifer, expressed by electrical conductivity [EC (lS/cm)], and to employ an integrated method to combine the advantages of neural network models utilizing the concept of committee machine. To develop the models, 93 data records of groundwater samples were collected from East Azarbaijan regional water company. The data set including Ca 2? , Mg 2? , Na ? , SO 4 2-and Cl -concentrations as the inputs and salinity [EC (lS/ cm)] as an output were divided into two subsets; training and testing based on cross validation approach. After training and testing of the models, the performance of the models were evaluated using root mean square errors (RMSE), determination coefficient (R 2 ) and mean absolute error (MAE). The performance criteria of the constructed neural network models showed that RBFNN model has the best performance in predicting salinity. The committee neural network (CNN) combined the results of salinity predicted from MLP, RBFNN and GRNN, each of them has a weight factor showing its contribution in overall prediction. The optimal weights were derived by a genetic algorithm (GA). The results of salinity prediction derived from CNN showed that the CNN performs better than any one of the individual ANNs acting alone for predicting groundwater salinity.

show abstract

“…The GRNN is a generalization of both radial basis function networks and probabilistic neural networks that can perform linear and nonlinear regression [9]. These feed-forward networks use basis function architectures which can approximate any arbitrary function between input and output vectors directly from training samples, and they can be used for multidimensional interpolation [7,12]. The main function of a GRNN is to estimate a linear or nonlinear regression surface on independent variables (input vectors) U, given the dependent variables (desired output vectors) X.…”

Section: Overview Of Methodsmentioning

confidence: 99%

MFFNN and GRNN Models for Prediction of Energy Equivalent Speed Values of Involvements in Traffic Accidents / Trafik Kazalarında tutulumunun Enerji Eşdeğer Hız Değerleri Tahmininde MFFNN ve GRNN Modelleri

Yılmaz¹,

Acı²,

Aydın³

2015

IJAET

View full text Add to dashboard Cite

Accident reconstruction is a scientific study field that depends on analysis, research and drawing. Scientific reconstruction of related traffic accident on computer eliminates making decisions depending on initiative or experience of the expert and yields impartial decisions and evidences especially on events like matter for the courts or forensic investigations. In this study, data collected from accident scene (police reports, skid marks, deformation situation of involvements, crush depth etc.) were inserted properly into the software called "vCrash" which is able to simulate the accident scene in 2D and 3D. Then, 784 parameters, related to calculating Energy Equivalent Speed (EES) with a prediction error, were prepared according to several accidents. These parameters were also used as teaching data for the Multi-layer Feed Forward Neural Network (MFFNN) and Generalized Regression Neural Network (GRNN) models in order to predict EES values of involvements, which give idea about severity and dissipation of deformation energy corresponding to the observed vehicle residual crush, without requirement of performing simulation for probable accidents in future. Using 10-fold cross validation on the dataset, standard error of estimates (SEE) and multiple correlation coefficients (R) of both models are calculated. The GRNN-based model yields lower SEE whereas the MFFNN-based model yields higher R.

show abstract

Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

Cited by 159 publications

References 34 publications

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

MFFNN and GRNN Models for Prediction of Energy Equivalent Speed Values of Involvements in Traffic Accidents / Trafik Kazalarında tutulumunun Enerji Eşdeğer Hız Değerleri Tahmininde MFFNN ve GRNN Modelleri

Contact Info

Product

Resources

About