Artificial intelligence for the prediction of water quality index in groundwater systems

被引：119

作者：

Sakizadeh M. ^{[1
]}

机构：

[1] Department of Environmental Sciences, Faculty of Sciences, Shahid Rajaee Teacher Training University, Tehran

来源：

Modeling Earth Systems and Environment | 2016年 / 2卷 / 1期

关键词：

Artificial neural network; Bayesian regularization; Early stopping; Ensemble method; Water quality index;

D O I：

10.1007/s40808-015-0063-9

中图分类号：

学科分类号：

摘要：

A study was initiated to predict water quality index (WQI) using artificial neural networks (ANNs) with respect to the concentrations of 16 groundwater quality variables collected from 47 wells and springs in Andimeshk during 2006–2013 by the Iran’s Ministry of Energy. Such a prediction has the potential to reduce the computation time and effort and the possibility of error in the calculations. For this purpose, three ANN’s algorithms including ANNs with early stopping, Ensemble of ANNs and ANNs with Bayesian regularization were utilized. The application of these algorithms for this purpose is the first study in its type in Iran. Comparison among the performance of different methods for WQI prediction shows that the minimum generalization ability has been obtained for the Bayesian regularization method (MSE = 7.71) and Ensemble averaging method (MSE = 9.25), respectively and these methods showed the minimum over-fitting problem compared with that of early stopping method. The correlation coefficients between the predicted and observed values of WQI were 0.94 and 0.77 for the test and training data sets, respectively indicating the successful prediction of WQI by ANNs through Bayesian regularization algorithm. A sensitivity analysis was implemented to show the importance of each parameter in the prediction of WQI during ANN’s modeling and showed that parameters like Phosphate and Fe are the most influential parameters in the prediction of WQI. © 2015, Springer International Publishing Switzerland.

引用

共 42 条

[1]

Ajorlo M., Ramdzani A., Assessment of stream water quality in tropical grassland using water quality index, Ecopersia, 2, pp. 427-440, (2014)

[2]

Anctil F., Perrin C., Andreassian V., Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models, Environ Model Soft, 19, pp. 357-368, (2004)

[3]

Beale M.H., Hagan M.T., Demuth H.B., Neural network toolbox user guide, (2013)

[4]

Bowden G.J., Maier H.R., Dandy G.C., Optimal division of data for neural network models in water resources applications, Water Resour Res, (2002)

[5]

Coulibaly P., Anctil F., Bobee B., Daily reservoir inflow forecasting using artificial neural networks with stopped training approach, J Hydrol, 230, pp. 244-257, (2000)

[6]

Daliakopoulos I.N., Coulibaly P., Tsanis I.K., Groundwater level forecasting using artificial neural networks, J Hydrol, 309, pp. 229-240, (2005)

[7]

Doan C.D., Liong S.Y., Generalization for multilayer neural network Bayesian regularization or early stopping, The 2Nd APHW Conference, (2004)

[8]

Dogan E., Sengorur B., Koklu R., Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique, J Environ Manage, 90, pp. 1229-1235, (2009)

[9]

Dwivedi S.L., Pathak V., A preliminary assignment of water quality index to Mandakini river, Chitrakoot, Ind. J Environ Prot, 27, pp. 1036-1038, (2007)

[10]

Gazzaz N.M., Yusoff M.K., Aris A.Z., Juahir H., Ramli M.F., Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors, Mar Pollut Bull, 64, pp. 2409-2420, (2012)

← 1 2 3 4 5 →