Artificial Neural Network Modeling for Predicting of some Ion Concentrations in the Karaj River

Document Type: Research Paper

Authors

Babol Noushiravani University of Technology, Babol, Iran

Abstract

The water quality of the Karaj River was studied through collecting 2137 experimental data set gained by 20 sampling stations. The data included different parameters such as T (temperature), pH, NTU (turbidity), hardness, TDS (total dissolved solids), EC (electrical conductivity) and basic anion, cation concentrations. In this study a multi-layer perceptron artificial neural network model was designed to predict the calcium, sodium, chloride and sulfate ion concentrations of the Karaj River. 1495 data set were used for training, 321 data set were used for test and 321 data set were used for validation. The optimum model holds sigmoid tangent transfer function in the middle layer and three different forms of the training function. The root mean square error (RMSE), mean relative error (MRE) and regression coefficient (R) between experimental data and model’s outputs were measured for training, validation and testing data sets. The results indicate that the ANN model was successfully applied for prediction of calcium ion concentration.

Keywords

Main Subjects


[1] Boyd, C. E. (2000). Water Quality: An introduction kluwer academic publishers. Norwell, Massachusetts, 2061.

[2] Chen, L. (2017). A Case study of dissolved oxygen characteristics in a wind-induced flow dominated shallow stormwater pond subject to hydrogen sulfide production (Doctoral dissertation, université d'Ottawa/University of Ottawa).

[3] World Health Organization. (2004). Guidelines for drinking-water quality (Vol. 1). World Health Organization.

[4] Chapman, D. V., World Health Organization. (1996). Water quality assessments: a guide to the use of biota, sediments and water in environmental monitoring.

[5] McCleskey, R. B. (2011). Electrical conductivity of electrolytes found in natural waters from (5 to 90) C. Journal of chemical and engineering data, 56(2), 317-327.

[6] Marandi, A., Polikarpus, M., Jõeleht, A. (2013). A new approach for describing the relationship between electrical conductivity and major anion concentration in natural waters. Applied geochemistry, 38, 103-109.

[7] Aghbashlo, M., Hosseinpour, S., Mujumdar, A. S. (2015). Application of artificial neural networks (ANNs) in drying technology: a comprehensive review. Drying technology, 33(12), 1397-1462.

[8] Mirarab, M., Sharifi, M., Ghayyem, M. A., Mirarab, F. (2014). Prediction of solubility of CO2 in ethanol–[EMIM][Tf2N] ionic liquid mixtures using artificial neural networks based on genetic algorithm. Fluid phase equilibria, 371, 6-14.

[9] Movagharnejad, K., Mehdizadeh, B., Banihashemi, M., Kordkheili, M. S. (2011). Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network. Energy, 36(7), 3979-3984.

[10] Zare, A. H., Bayat, V. M., Daneshkare, A. P. (2011). Forecasting nitrate concentration in groundwater using artificial neural network and linear regression models. International agrophysics, 25(2), 187-192.

[11] Zare, A. H., Yazdani, V., Azhdari, K. H. (2009). Comparative study of four meteorological drought index based on relative yield of rain fed wheat in Hamedan province.  Physical geography research quarterly, 69, 35-49.

[12] Mehrdadi, N., Hasanlou, H., Jafarzadeh, M. T., Hasanlou, H., Abdolabadi, H. (2012). Simulation of low TDS and biological units of Fajr industrial wastewater treatment plant using artificial neural network and principal component analysis hybrid method. Journal of water resource and protection, 4(6), 370.

[13] Moghaddamali, F., Movagharnejad, K. (2014). Predicting electrical conductivity in Jajrud river by an artificial neural network. Caspian journal of applied sciences research, 3(11), 21-29.

[14] Demuth, H., Beale, M., Hagan, M. Neural network toolbox: for use with MATLAB2000. The mathworks.

[15] Coppola Jr, E., Szidarovszky, F., Poulton, M., Charles, E. (2003). Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions. Journal of hydrologic engineering, 8(6), 348-360.

[16] Coulibaly, P., Anctil, F., Aravena, R., Bobée, B. (2001). Artificial neural network modeling of water table depth fluctuations. Water resources research, 37(4), 885-896.