Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Yeşilnacar, Mehmet İ̇rfan; Şahinkaya, Erkan; Naz, Muhsin; Özkaya, Bestamin

doi:10.1007/s00254-007-1136-5

Cited by 121 publications

(52 citation statements)

References 19 publications

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“…The artificial neural network employed was a multilayer backpropagation network, which has been used successfully in several studies (Garcia & Shigidi, 2005, Kuo et al, 2003, Helle et al, 2001Yesilnacar et al, 2007;Yetilmezsoy & Demirel, 2007). The important feature of this network is its ability to self-adapt the weights of neurons in intermediate layers to learn the relationship between a set of patterns given as examples and their corresponding outputs, so that after having been trained, it can apply the same relationship to new input vectors and produce appropriate outputs from inputs that the system has never seen before, a feature known as the generalizability of an ANN (Mehrotra et al, 1997).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Artificial Neural Network Models for Prediction of Ozone Concentrations in Guadalajara, Mexico

García¹,

Rodríguez²,

Tenorio³

2011

Air Quality-Models and Applications

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Section: Artificial Neural Networkmentioning

confidence: 99%

Artificial Neural Network Models for Prediction of Ozone Concentrations in Guadalajara, Mexico

García¹,

Rodríguez²,

Tenorio³

2011

Air Quality-Models and Applications

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“…ANNs have been used to simulate the effect of climate change on discharge and the export of dissolved organic carbon and nitrogen from river basins (Clair and Ehrman 1996), predict salinity of groundwater and rivers (Maier and Dandy 1996;Huang and Foo 2002;Nasr and Zahran 2014;Ravansalar and Rajaee 2015), simulate and forecast residual chlorine concentrations within urban water systems (Rodriguez and Serodes 1998), prediction of arsenic, sulfate and nitrate concentrations in groundwater (Yesilnacar et al 2008;Yesilnacar et al 2012;Chang et al 2010), spatial distribution of groundwater quality (Khashei-Siuki and Sarbazi 2013) and determine the leachate amount from municipal solid waste landfill (Karaca and Ozkaya 2006).…”

Section: Introductionmentioning

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.

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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.

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“…The number of neurons, training algorithms and transfer function in the hidden layer has an important impact on network training and prediction results (Yesilnacar et al 2007). To determine the above parameters, we adopt a trial-and-error method (Raman and Sunilkumar, 1995) to screen optimal network structure based on relevant indicators outputted by BP neural network in the training process.…”

Section: Establishment Of Neural Network Predictionmentioning

confidence: 99%

Application of Grey Prediction and BP Neural Network in Hydrologic Prediction

Huang

Jin

Xiang

et al. 2015

MATEC Web of Conferences

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Based on the data of annual runoff at Boluo Hydrologic Station in the Dongjiang River of Guangdong Province from 1954 to 2010, this paper establishes a prediction model of an annual runoff through three methods, that is, the regression analysis, the grey theory and the neural network. The prediction model which is established by the regression analysis method has passed F-statistical test (P=0.05), but the relative error of predicted value at 30% of the data point is over ±30%, and its prediction precision is general. The precision of the residual prediction model of an annual runoff GM (1, 1) that established based on the grey theory is obviously better than that of the former one; the relative error of predicted value at only 10% of the data point is over ±30%; the Nash statistical coefficient (NS) of predicted value and measured value is 0.627, and the correlation coefficient (R 2 ) is 0.774. For the prediction model of BP neural network, the relative error at about 5% of the data point is over ±30%, and NS=0.66, R 2 =0.853. In general, the precision and the reliability of the neural network prediction model of an annual runoff are the best.

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Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Cited by 121 publications

References 19 publications

Artificial Neural Network Models for Prediction of Ozone Concentrations in Guadalajara, Mexico

Artificial Neural Network Models for Prediction of Ozone Concentrations in Guadalajara, Mexico

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

Application of Grey Prediction and BP Neural Network in Hydrologic Prediction

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