Use of ANFIS/Genetic Algorithm and Neural Network to Predict Inorganic Indicators of Water Quality

Document Type : Research Paper


Department of Chemical Engineering, Faculty of Energy, Kermanshah University of Technology, Kermanshah, Iran


The present research used novel hybrid computational intelligence (CI) models to predict inorganic indicators of water quality. Two CI models i.e. artificial neural network (ANN) and a hybrid adaptive neuro-fuzzy inference system (ANFIS) trained by genetic algorithm (GA) were used to predict inorganic indicators of water quality including total dissolved solids (TDS), total hardness (TH), total alkalinity (TAlk), and electrical conductivity (σ). The study was conducted on samples collected from water wells of Kermanshah province through analyzing water parameters including pH, temperature (T), and the sum of mill equivalents of cations (SC) and anions (SA). A multilayer perceptron (MLP) structure was used to forecast inorganic indicators of water quality using the ANN approach. A MATLAB code was used for the proposed ANFIS model to adjust and optimize the ANFIS parameters during the training process using GA. The accuracy of the generated models was described using various evaluation techniques such as mean absolute error (MAE), correlation factor (R), and mean relative error percentage (MRE%). The results showed that both methods were suitable for predicting inorganic indicators of water quality. Moreover, the comparison of the two methods showed that the predicted values obtained from the ANFIS/GA model were better than those obtained from the ANN approach.


[1] Khalil B, Ouarda TB, St-Hilaire A, Chebana F. A statistical approach for the rationalization of water quality indicators in surface water quality monitoring networks. Journal of Hydrology. 2010 May 28;386(1-4):173-85.
[2] Behmel S, Damour M, Ludwig R, Rodriguez MJ. Water quality monitoring strategies—A review and future perspectives. Science of the Total Environment. 2016 Nov 15;571:1312-29.Hosseini-Dastgerdi Z, Meshkat SS. An experimental and modeling study of asphaltene adsorption by carbon nanotubes from model oil solution. Journal of Petroleum Science and Engineering. 2019 Mar 1;174:1053-61.
[3] Dimitrovska O, Markoski B, Toshevska BA, Milevski I, Gorin S. Surface water pollution of major rivers in the Republic of Macedonia. Procedia Environmental Sciences. 2012 Jan 1;14:32-40.
[4] Ouyang Y. Evaluation of river water quality monitoring stations by principal component analysis. Water research. 2005 Jul 1;39(12):2621-35.
[5] Park SY, Choi JH, Wang S, Park SS. Design of a water quality monitoring network in a large river system using the genetic algorithm. Ecological modelling. 2006 Dec 1;199(3):289-97.
[6] House MA. Public perception and water quality management. Water Science and Technology. 1996 Jan 1;34(12):25-32.
[7] Wu Q, Xia X, Mou X, Zhu B, Zhao P, Dong H. Effects of seasonal climatic variability on several toxic contaminants in urban lakes: Implications for the impacts of climate change. Journal of Environmental Sciences. 2014 Dec 1;26(12):2369-78.
[8] Delpla I, Benmarhnia T, Lebel A, Levallois P, Rodriguez MJ. Investigating social inequalities in exposure to drinking water contaminants in rural areas. Environmental Pollution. 2015 Dec 1;207:88-96.
[9] Thompson MY, Brandes D, Kney AD. Using electronic conductivity and hardness data for rapid assessment of stream water quality. InWorld Environmental and Water Resources Congress 2010: Challenges of Change 2010 (pp. 3356-3365)..
[10] Kney AD, Brandes D. A graphical screening method for assessing stream water quality using specific conductivity and alkalinity data. Journal of environmental management. 2007 Mar 1;82(4):519-28.
[11] Gergel SE, Turner MG, Miller JR, Melack JM, Stanley EH. Landscape indicators of human impacts to riverine systems. Aquatic sciences. 2002 Jun 1;64(2):118-28.
[12] Aghel B, Rezaei A, Mohadesi M. Modeling and prediction of water quality parameters using a hybrid particle swarm optimization–neural fuzzy approach. International Journal of Environmental Science and Technology. 2019 Aug 1;16(8):4823-32.
[13] Najah A, El-Shafie A, Karim OA, Jaafar O, El-Shafie AH. An application of different artificial intelligences techniques for water quality prediction. International Journal of Physical Sciences. 2011 Oct 2;6(22):5298-308.
[14] Ding YR, Cai YJ, Sun PD, Chen B. The use of combined neural networks and genetic algorithms for prediction of river water quality. Journal of applied research and technology. 2014;12(3):493-9.
[15] Reichert P, Vanrolleghem P. Identifiability and uncertainty analysis of the river water quality model no. 1 (RWQM1). Water Science and technology. 2001 Apr;43(7):329-38.
[16] Chapra SC, Pelletier GJ, Tao H. QUAL2K: a modeling framework for simulating river and stream water quality, version 2.11: documentation and users manual. Civil and Environmental Engineering Dept., Tufts University, Medford, MA. 2008:1-09.
[17] Wool TA, Ambrose RB, Martin JL, Comer EA, Tech T. Water quality analysis simulation program (WASP). User’s Manual, Version. 2006;6.
[18] Suen JP, Eheart JW. Evaluation of neural networks for modeling nitrate concentrations in rivers. Journal of water resources planning and management. 2003 Nov;129(6):505-10.
[19] Dahiya S, Singh B, Gaur S, Garg VK, Kushwaha HS. Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Materials. 2007 Aug 25;147(3):938-46.
[20] Chen YH, Chang FJ. Evolutionary artificial neural networks for hydrological systems forecasting. Journal of Hydrology. 2009 Mar 30;367(1-2):125-37.
[21] Icaga Y. Fuzzy evaluation of water quality classification. Ecological Indicators. 2007 Jul 1;7(3):710-8.
[22] Kisi O, Ozkan C, Akay B. Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. Journal of Hydrology. 2012 Mar 27;428:94-103.
[23] Nayak PC, Sudheer KP, Ramasastri KS. Fuzzy computing based rainfall–runoff model for real time flood forecasting. Hydrological Processes: An International Journal. 2005 Mar 15;19(4):955-68.
[24] Areerachakul S. Comparison of ANFIS and ANN for estimation of biochemical oxygen demand parameter in surface water. International Journal of Chemical and Biological Engineering. 2012 Apr 26;6:286-90.
[25] Yan H, Zou Z, Wang H. Adaptive neuro fuzzy inference system for classification of water quality status. Journal of Environmental Sciences. 2010 Dec 1;22(12):1891-6.
[26] Ahmed AM, Shah SM. Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. Journal of King Saud University-Engineering Sciences. 2017 Jul 1;29(3):237-43.
[27] Da Silva IN, Spatti DH, Flauzino RA, Liboni LH, dos Reis Alves SF. Artificial neural network architectures and training processes. InArtificial neural networks 2017 (pp. 21-28). Springer, Cham.
[28] Haykin SS. Neural networks and learning machines/Simon Haykin..
[29] Ram M, editor. Advanced Fuzzy Logic Approaches in Engineering Science. IGI Global; 2018 Sep 14.
[30] Sivanandam SN, Sumathi S, Deepa SN. Introduction to fuzzy logic using MATLAB. Berlin: Springer; 2007 Jan.