Comparison of the support vector machine and radial function neural network models in predicting of SiminehRood river water quality Iran.

Document Type : Research Paper

Authors

1 MSc student, Department of Irrigation & Reclamation Engineering, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.

2 Assistant Professor, Department of water sciences and engineering, faculty of agriculture, University of Kurdistan, Sannandaj, Iran.

3 Professor, Department of Irrigation & Reclamation Engineering, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.

Abstract

In this study, the performance of Support Vector Machine (SVM) and Radial Base Neural Network approach in predicting the water quality of SiminehRood River was examined. For this purpose, the Sodium Adsorption Ratio (SAR) and Chlorine ions were considered as indicators of water quality in agricultural use. Sodium, calcium, magnesium, pH, Ec, and river flow rate were utilized as input monthly parameters throughout a 12-year period (2003-2014). The results evaluated based on correlation coefficient, root means square error and mean absolute error. The results of the validation period in 4 stations of Pol Bukan, Dashband Bukan, Ghezel Gonbad and Kaullan showed that the SVM model in comparison with the neural network of the radial base function, has higher correlation coefficient (SVM: 0.71 to 0.94, RBF: 0.3 to 0.5), the lowest root means square error (SVM: 0.028 to 0.075 mg/l, RBF: 0.0672 to 0.317 mg/l), a lower absolute mean error (SVM: 0.003 to 0.033 mg/l, RBF: 0.087 to 0.19 mg/l) for the chlorine ion parameter and in the same order SVM values: 0.63 to 0.88 and RBF: 0.21 to 0.38, SVM: 0.0013 to 0.0282 mg/l and RBF: 0.047 to 0.025 mg/l, SVM: 0.0085 to 0.046 mg/L and RBF: 0.0653 to 0.0996 mg/l for sodium absorption ratio.Therefore, the Support Vector Machine model has better accuracy and performance for predicting water quality parameters of SiminehRood River than the Radial Basis Function Network.

Keywords

Main Subjects

References

Abbasian, M., &Shahraki, A. (2020). Modeling and comparison of GMDH and RBF artificial neural networks in predicting short-term drinking water demand in Zahedan. Iranian Journal of Irrigation and Water Engineering, 10(3), 248-261. (In Persian).
Abobakr Yahya, A. S., Ahmed, A. N., Binti Othman, F., Ibrahim, R. K., Afan, H. A., El-Shafie, A., Fai, C. M., Hossain, M. S., Ehteram, M., & Elshafie, A. (2019). Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios: Water, 11(6), 1231.
Akhoni Pourhosseini, F., & Ebrahimi, K. (2019). Shannon’s Entropy Evaluation on Determination of Surface Water Quality (Case Studies: Karun and Babolrood Rivers). Iranian Journal of Water and Irrigation Management, 9(2), 171-183. (In Persian).
Aldhyani, T. H., Al-Yaari, M., Alkahtani, H., & Maashi, M. (2020). Water quality prediction using artificial intelligence algorithms: Applied Bionics and Biomechanics, v. 2020.
Arabgol, R.F., & Sartaj, M. (2012). Evaluation of efficiency of support vector machines in estimating nitrate concentration in groundwater. In: Sixth National Congress of Civil Engineering, 26-27 April, Semnan University, Iran. (In Persian).
Banihabib, M. E. & Arabi, A. (2008). Artificial Neural Network Model for Determining Flood Warning Time in Golabdereh-Darband Basin. In: Third Iranian Water Resources Management Conference, 14-16 Oct, Tabriz University, Iran. (In Persian).
Ehteshami, M., Dolatabadi Farahani, N., & Tavassoli S. (2016). Simulation of nitrate contamination in groundwater using artificial neural networks. Modeling Earth Systems and Environment, 28, 10(2).
Fathian, H., & Hormozinezhad, A. (2012). Prediction of quantitative and qualitative parameters of Karun river flow using artificial neural network. Journal of Wetland, Islamic Azad University of Ahvaz, (8)5, 43-29. (In Persian).
Isazadeh, M., Biazar, S., Ashrafzadeh, A., & Khanjani, R. (2019). Estimation of Aquifer Qualitative Parameters in Guilans Plain Using Gamma Test and Support Vector Machine and Artificial Neural Network Models. Environmental Science and Technology, 21(2), 1-21. (In Persian).
Kianian, A., Mobarghaee Dinan, N., & Hashemi, H. (2016). Zoning of soils by irrigation with sewage using interpolation method (IDW) (Case study: southern city of Ray). Journal of Environmental Research, 7 (14), 81-90. (In Persian).
Komasi, M., Goodarzi, H., & Behnia, A. (2017). Investigation of spatial-temporal fluctuations of groundwater water table by support vector and kriging machine (IDW). Journal of Soil and Water Conservation Science (Agriculture and Natural Resources), 24(4), 71-80. (In Persian).
Mohammadi, P., & Ebrahimi, K. (2018). Estimation of electrical conductivity of Aharchai River using neural network models and adaptive neural-fuzzy inference. In: National Hydraulic Conference of Iran, 4-6 Sep, Shahre kord University, Iran. (In Persian).
Morshedy, A., & Memarian, H. (2015). A New Method of Generalized Radial Basis Function Network to Interpolate Regional Variables in Geosciences. Scientific Quarterly Journal, Geosciences, 24 (96), 107-116.
Nikpour, M., & MahmodiBabelan, S. (2019). Compare the performance intelligent routing models daily river flow (Case study: River Balkhlouchay, Ardabil). Iranian Journal of Irrigation and Water Engineering, 8 (32), 64-78. (In Persian).
Pirali Zefreh E, A.R., Hedayati, A., Pourmanafi, S., Beyraghdar, O., & Ghorbani,R. (2020). Evaluation of the efficiency of support vector machine in predicting changes in water quality parameters (Case study: Choghakhor International Wetland). Iranian Journal of Aquatic Ecology, 10(1), 23-34. (In Persian).
Rezaei, A., & Mirmohammadi Meybodi, S.A.M. (2014). Statistics and Probability: used in agriculture. Isfahan: Academic Center for Education, Culture and Research, Isfahan University of Technology Press. (In Persian).
Salavati, A., Banihabib, M. E., & Soltani, J. (2016).  Hybrid Model for Reservoir Operation Optimization. In: Water sciences and Engineering Conference, 8-9 June, Shahid Beheshti Conference center, Tehran, Iran. (In Persian).
Santin, I. (2015). Effluent Predictions in Wastewater Treatment Plants for the Control Strategies Selection, Journal of Bilbao, 2, 1009-1016.
Shahinejad, B., & Dehghani, R. (2017). Evaluation and Performance of Support Vector Machine Model in Estimation of Suspended Sediment. Journal of Irrigation and Water Engineering, 8(1), 30-42. (In Persian).
Vapnik,V. (1995). The Nature of statistical learning Theory. New York: Springer Press.
Vapnik,V. (1998). Statistical learning Theory. New York, NY, USA: John Wiley Press.
www.analyticsvidhya.com/blog/2014/10/support-vector-machine-simplified (Last access 2021 May 14).
Xin, X., Li, K., Finlayson, B., & Yin, W. (2015). Evaluation, prediction and protection of water quality in Danjiangkou Reservoir. Water science and Engineering, 8, 30-39.
Yilmaz, I., & Keynar, O. (2011). Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications, 38(5), 5958-5966.
Water and Irrigation Management
Volume 11, Issue 3
November 2021
Pages 409-419

Files

Share

How to cite

Statistics