Estimating Water Quality Parameters Using a Hybrid Extreme Learning Machine Method and Wavelet Theory

Document Type : Research Article


1 Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology

2 Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology.


Each of the various uses of water, such as agriculture, drinking, industry, etc., require water with a specific quality that is characterized by repeated sampling, testing, and analysis of the results. However, the cost of sampling surface water, measuring quality parameters in the laboratory environment, human errors are the most important problems in estimating the concentration of water qualitative parameters. For this purpose, there are several methods for modeling the water quality parameters. In this regard, the data mining methods have been considered by the researchers in recent decades. Therefore, in this research, the main purpose is to estimating and modeling water quality parameters using modern data mining methods and improve the performance of data mining methods with the aim of wavelet theory and compare them with other commonly used data mining methods. In other words, extreme learning machines (ELM) and multi-layer perceptron (MLP) method will be used to model water quality parameters. The evaluation of these two models was performed by statistical criteria Correlation Coefficient (R), root mean square error (RMSE) and mean absolute error (MAE) and relative standard error (RSE) for statistical data of 20 years. According to the results, it was found that the ELM method has been able to averagely provide a correlation coefficient of 0.97. Although both models yielded acceptable results, the results showed that the ELM model has higher accuracy than the MLP model for prediction of water quality parameters.


Main Subjects

]. Soleimani S, Bozorg-Haddad O, Moravej M. Modeling Water Quality Parameters Using Data-driven Methods. Journal of Water and Soil. 2016;30(3):743-57.
[2]. Banejad H, Kamali M, Amirmoradi K, Olyaie E. Forecasting Some of the Qualitative Parameters of Rivers Using Wavelet Artificial Neural Network Hybrid (W-ANN) Model. Iran j Health & Environ. 2012;6(3):277-94.
[3]. Sattari MT, Abbasgoli Naebzad M, Mirabbasi Najafabadi R. Surface water quality prediction using decision tree method. journal of Irrigation & Water Engineering 2014;4(15):76-88.
[4]. Ahmadi MZ, Behzadi S. The process of evaluation of magnesium changes using the neural network and spatial information system in the villages of Gonbad city (Golestan province). Scientific - Research Quarterly of Geographical Data (SEPEHR). 2016;25(99):29-42.
[5]. Asadollahfardi G, Taklify A, Ghanbari A. Application of artificial neural network to predict TDS in Talkheh Rud River. Journal of Irrigation and Drainage Engineering. 2011;138(4):363-70.
[6]. Mahmoudi N, Orouji H, Fallah-Mehdipour E. Integration of shuffled frog leaping algorithm and support vector regression for prediction of water quality parameters. Water resources management. 2016;30(7):2195-211.
[7]. Bozorg-Haddad O, Soleimani S, Loáiciga HA. Modeling Water-Quality Parameters Using Genetic Algorithm–Least Squares Support Vector Regression and Genetic Programming. Journal of Environmental Engineering. 2017;143(7):04017021.
[8]. Alizamir M, Kisi O, Zounemat-Kermani M. Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrological Sciences Journal. 2018;63(1):63-73.
[9]. Najafzadeh M, Ghaemi A, Emamgholizadeh S. Prediction of water quality parameters using evolutionary computing-based formulations. International Journal of Environmental Science and Technology. 2018:1-20.
[10]. Heddam S, Kisi O. Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology. 2018;559:499-509.
[11]. Haykin S. Neural networks: Prentice hall New York; 1994.
[12]. Coppola E, Poulton M, Charles E, Dustman J, Szidarovszky F. Application of artificial neural networks to complex groundwater management problems. Natural Resources Research. 2003;12(4):303-20.
[13]. Lee T, Jeng D, Zhang G, Hong J. Neural network modeling for estimation of scour depth around bridge piers. Journal of hydrodynamics. 2007;19(3):378-86.
[14]. Huang G-B, Siew C-K. Extreme learning machine with randomly assigned RBF kernels. International Journal of Information Technology. 2005;11(1):16-24.
[15]. Ding S, Guo L, Hou Y. Extreme learning machine with kernel model based on deep learning. Neural Computing and Applications. 2017;28(8):1975-84.
[16]. Ertuğrul ÖF, Kaya Y. A detailed analysis on extreme learning machine and novel approaches based on ELM. American Journal of computer science and engineering. 2014;1(5):43-50.
[17]. Zhang L, Zhou W, Jiao L. Wavelet support vector machine. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2004;34(1):34-9.
[18]. Kisi O, Cimen M. Precipitation forecasting by using wavelet-support vector machine conjunction model. Engineering Applications of Artificial Intelligence. 2012;25(4):783-92.
[19]. Wang W-C, Chau K-W, Cheng C-T, Qiu L. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of hydrology. 2009;374(3-4):294-306.
Volume 6, Issue 2
July 2019
Pages 369-383
  • Receive Date: 22 November 2018
  • Revise Date: 14 March 2019
  • Accept Date: 14 March 2019
  • First Publish Date: 22 June 2019