Comparison of wavelet-MLP and wavelet-GMDH models in forecasting EC and SAR at Zayandeh-Rood River

Document Type : Original Article


Department of Water Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran


Increasing water demand and water pollution due to the development of agricultural, urban and industrial activities have caused environmental problems all over the world. The significant increase in water pollution and the diversity of various urban, agricultural and industrial pollutants made the qualitative management of water resources inevitable. Short-term and long-term accurate forecasts of river quality parameters are essential for designing hydraulic structures, irrigation planning, optimal utilization of reservoirs and environmental planning. Given the stochastic characteristics of the hydrological events, forecasting the future status of surface waters is always associated with uncertainties. The purpose of the present study was to investigate the performance of two types of artificial neural networks, namely MLP and GMDH, combined with discrete wavelet transform (DWT), to forecast two important quality parameters, electrical conductivity (EC) and sodium adsorption ratio (SAR) at Zayandeh-Rood River in 1, 2 and 3 months ahead.
Material and methods:
In this study, water quality data (EC and SAR) of Zayandeh-Rood River at Zaman Khan Station was used from 1363 to 1384. From 21 years of data, 15 years (approximately 70%) were used for training and 7 years (30%) were used to test the developed models. Two types of mother wavelet dmey and db4 were evaluated. Statistical parameters such as RMSE and R2 were used to evaluate the performance of the models.
Results and discussion:
The results showed that the use of discrete wavelet transform improves the performance of the models. Various combinations of input data (various delays) and two types of mother wavelets were evaluated. The results showed that wavelet-MLP and wavelet-GMDH hybrid models outperform single MLP and single GMDH models at all forecasting intervals. The results of the single MLP and GMDH models were only effective in forecasting SAR one month ahead but practically could not forecast two and three months later. In the EC parameter, the MLP and GMDH models performed better. Also, the results showed that the use of annual time lags does not increase the accuracy and in some cases even reduces it. The study of the types of mother wavelets also showed that the dmey wavelet is the most suitable wavelet type to forecast EC and SAR qualitative parameters. The comparison between wavelet-MLP and wavelet-GMDH models showed the relative superiority of the former model. By increasing the forecast period from one month to three months ahead, the accuracy of the models decreased. This decrease in precision was higher in forecasting SAR parameter, e.g. in the one month forecast, R2 was 0.936 and in the 3 months ahead forecasts it was reduced to 0.516. In the EC parameter, the R2 fell to 0.641 in 3 months ahead forecasting.Conclusion: The results of this study can be used as a basis for future planning for water quality. It is suggested that the model presented in this study should be considered in other rivers. Also, the combination of other artificial intelligent models such as ANFIS and SVM with wavelet transform can be evaluated.


  1. Abdollahi Asad Abadi, S., Dinpaghoh, Y., MirAbbasi, R., 2014. Forecasting of daily mean discharge at Behesht Abad River using Wavelet transform. Water and Soil Journal. 28, 534-545. (In Persian with English abstract).
  2. Adamowski, J. and Chan, H.F., 2011. A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology. 407, 28-40.
  3. Ahmadi, M., Parsaee, A. and Ghaderi, K., 2012. Extracting the prediction of pollution coefficient in rivers using the GMDH group data group and comparison with experimental relationships. 11th Iranian Hydraulic Conference. Urmia. Iran p. 220. (In Persian with English abstract).
  4. Badrzadeh, H., 2014. River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques. PHD Thesis. Curtin University. Australia.
  5. Barzegar, R., Moghaddam, A.A., Adamowski, J. and Ozga-Zielinski, B., 2018. Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model. Stochastic Environmental Research and Risk Assessment. 32, 799-813.
  6. Haghi Zadeh, A., Yousefi, H., Yarahmadi, Y., Normohammadi, P. and Alijani, R., 2017. Forecasting and Trend Analytics of Water quality parameters using ARIMA series Models in Kahman river watershed. Ecohydrology. 4, 65-73. (In Persian with English abstract)
  7. Hassanzadeh, Y. Kordan, A. Fakheri Fard, A., 2012. Prediction of drought, the use of wavelet-neural networks, genetic algorithms and hybrid models. Journal of Water and Wastewater. 23, 59-48. (In Persian with English abstract)
  8. Hayking, S., 1999. Neural networks: A comprehensive foundation, 2nd Ed. Prentice-Hall, N.J.
  9. Ivakhnenko, A.G. and Ivakhnenko, G. A., 1995. The review of problems solvable by algorithms of the Group Method of Data Handling (GMDH)”, Pattern recognition and image analysis. 5, 527-535.
  10. Ivakhnenko, A.G., 1968. The group method of data handling–a rival of the method of stochastic approximation. Sovit automatic control avtomatika. 1, 43-55.
  11. Iyengar, S. S. Cho, E. C. and Phoha, V.V., 2002. Foundations of Wavelet Networks and Applications. Chapman & Hall/CRC Press.
  12. Jafar Zadeh, N., Kabi, H. and Sepehr Far, K., 2006. Application Feasibility and Selection of the Most Appropriate Water River Water Quality Index Case Study: Zohreh River, 7th conference on river engineering, Ahvaz, Iran p. 211. (In Persian with English abstract).
  13. Karami, M., KashefiPour, M., Mazad, H. and Foroughi, H., 2006. Forecasting of Karoon river quality using ANN. 7th conference on river engineering, Ahvaz, Iran. p. 321. (In Persian with English abstract).
  14. Kazemi Poshtmasari, H., Tahmasby Servestani, Z., Kamkar, B., Shtayy, SH. And Sadegi, S., 2012. Evaluation of geostatistics methods for estimating and zoning primary macro nutrients in some agricultural lands in Golestan province. Science Journal. 22, 121-129. (In Persian with English abstract).
  15. Khani, S. and Rajaee, T., 2017. Modelling of Dissolved Oxygen Concentration and Its Hysteresis Behaviour in Rivers Using Wavelet Transform‐Based Hybrid Models. Clean–Soil, Air, Water. 45, 212-220.
  16. Kişi, Ö., 2011. Evapotranspiration modelling using a wavelet regression model. Irrigation science. 29, 241-252.
  17. Kurunç, A., Yürekli, K. and Çevik, O., 2005. Performance of two stochastic approaches for forecasting waterquality and streamflow data from Yesilirmak River, Turkey. Environmental Modelling Software. 20, 1195–1200.
  18. Mihoub R, Chabour N, Guermoui, M., 2016. Modelling soil temperature based on Gaussian process regression in a semi-arid-climate, case study Ghardaia, Algeria Geomechanics and Geophysics for Geo-Energy and Geo-Resources. 2, 397-403.
  19. Najafzadeh, M. and Barani, G.A., 2011. Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers. Scientia Iranica. 18, 1207-1213.
  20. Najafzadeh, M., Barani, Gh-A. and Azamathulla. H.Md., 2013a. GMDH to Predict Scour Depth around Vertical Piers in Cohesive Soils. Applied Ocean Research. 40, 35-41.
  21. Najafzadeh, M., Barani, Gh-A. and Hessami-Kermani, M., 2013b. Abutment scour in live-bed and clear-water using GMDH Network. Water Science and Technology. 67, 1121-1128.
  22. Najafzadeh, M., G.-A. Barani, and Azamathulla, H., 2014. Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling. Neural Computing and Applications. 24, 629-635.
  23. Najah, A, El-Shafie A, Karim O, Jaafar O, El-Shafie AH., 2011. An application of different artificial intelligences techniques for water quality prediction. Journal of the Physical Sciences. 6, 5298-308.
  24. Najah, A., Elshafie, A., Karim, O.A. and Jaffar, O., 2009. Prediction of Johor River water quality parameters using artificial neural networks. European Journal of Scientific Research. 28, 422-435.
  25. Nourani, V., Hosseini Baghanam, A., Adamowski, J. and Kisi, O., 2014. Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. Journal of Hydrology. 514, 358-377.
  26. Partal, T., 2015. Comparison of wavelet based hybrid models for daily evapotranspiration estimation using meteorological data. KSCE Journal of Civil Engineering. 21, 1-9.
  27. Patil, A.P. and Deka, P.C., 2015. Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India. Neural Computing and Applications, 22, 1-11.
  28. Rajaee T, Rahimi Benmaran R. and Jafari, H., 2015. Prediction of quality parameters (NO3, DO) of Karaj River using ANN, MLR, and Denoising-based combined wavelet-neural network based on Models. Iranian Journal of health and environment. 4, 511-530. (In Persian with English abstract).
  29. Ramana, R.V., Krishna, B., Kumar, S.R. and Pandey, N.G., 2013. Monthly rainfall prediction using wavelet neural network analysis. Water resources management. 27, 3697-3711.
  30. Rioul, O.M. Vetterli M., 1991. Wavelets and signal processing. IEEE SP Magazine: 14–38
  31. Zaman Zad, S., Ghavidel1, S. and Zeinalzadeh, K., 2015 Estimation of Rivers Dissolved Solids TDS by Soft Computing (Case Study: Upstream of Boukan Dam). Journal of soil and water. 29, 1234-1245. (In Persian with English abstract).
  32. Sattari, M.T, Abbasgoli, M. and Mirabbasi Najafabadi, R., 2014. Surface water quality prediction using decision tree method. Journal of Water and Irrigation Engineering. 4, 76-88. (In Persian with English abstract).
  33. Yaseen, Z.M., Ramal, M.M., Diop, L., Jaafar, O., Demir, V. and Kisi, O., 2018. Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation. Water Resources Management. 32, 2227-2245.