• AMERA ISMAIL MELHUM Dept. of Computer Science, College of Science, University of Duhok, Kurdistan Region-Iraq
  • JWAN ABDULKHALIQ MOHAMMED Dept. of Computer Science, College of Science, University of Duhok, Kurdistan Region-Iraq
Keywords: Water quality Index:, Estimation:, ANFIS:, Duhok camps:, RMSE:


The most appropriate method of communicating water quality situation of water bodies is the Water Quality Index (WQI); while user participation and dealing with uncertainty are required for the evaluation of WQI. The aim of WQI is to convert complicated water quality data to information which can be used and understood by users. This index is vital for users to know the gradation of suitable (fresh) water and unsuitable water which might be poisonous and cause serious diseases sometimes. The index might also be used to test the water quality before drilling water wells which are costly and can be really harmful to the environment; accordingly, costs and risks can be reduced a great deal. Lately, the algorithms of artificial intelligence which are suitable for nonlinear prediction and dealing with uncertain domains have been implemented in different fields of water quality estimation. The purpose of this study is to estimate the water quality index using data sets obtained from 22 camps located in six districts in Duhok city for the period March to August 2018. The data sets contain six water quality parameters which are Nitrates (NO3), Sulphate (SO4), Total Hardness (TH), PH, Total ALkalinity (T. AL) and Calcium (Ca). This paper uses the application of Adaptive Neuro Fuzzy Inference System (ANFIS) for modeling the estimation of water quality index. This model is utilized to train, test and check the index. Statistical criteria such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used to assess the performance of the ANFIS model. Investigations show that for estimation WQI, the RMSE values are 0.0346, 0.2109 and 0.0403 for training, checking and testing stages, respectively. While, the values of MSE are 0.0012, 0.0445 and 0.0016 for training, checking and testing stages, respectively. Based on the results of the criteria, the ANFIS estimation model has the ability to forecast the water quality index for Duhok camps with reasonable accuracy, and it is useful and valuable for the estimation of WQI


Download data is not yet available.


 Ahmed, A. M., & Shah, S. M. A. (2017). 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, 29(3), 237-243.
 Al-Obaidy, A. H. M. J., Al-Janabi, Z. Z., & Shakir, E. (2015). Assessment of water quality of Tigris River within Baghdad City. Mesop. Environ. j, 1(3), 90-98.
 Babbar, R., & Babbar, S. (2017). Predicting river water quality index using data mining techniques. Environmental Earth Sciences, 76(14), 504.
 Bezdek, J. C. (1981). Objective function clustering. In Pattern recognition with fuzzy objective function algorithms (pp. 43-93). Springer, Boston, MA
 Chau, K. W. (2006). A review on integration of artificial intelligence into water quality modelling. Marine pollution bulletin, 52(7), 726-733.
 Eassa, A. M., & Mahmood, A. A. (2012). An Assessment of the treated water quality for some drinking water supplies at Basrah. Journal of Basrah Researches (Sciences), 38(3A), 95-105.
 Horton, R. K. (1965). An index number system for rating water quality. Journal of Water Pollution Control Federation, 37(3), 300-306.
 Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
 Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
 Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence.
 Jang, J. S., & Sun, C. T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378-406.
 Kangabam, R. D., Bhoominathan, S. D., Kanagaraj, S., & Govindaraju, M. (2017). Development of a water quality index (WQI) for the Loktak Lake in India. Applied Water Science, 7(6), 2907-2918.
 Kassem, Y., Çamur, H., & Esenel, E. (2017). Adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) prediction of biodiesel dynamic viscosity at 313 K. Procedia Computer Science, 120, 521-528.
 Khudair, B. H. (2018). Water Quality Assessment and Total Dissolved Solids Prediction using Artificial Neural Network in Al-Hawizeh Marsh South of Iraq. Journal of Engineering, 24(4), 147-156.
 Kumar, P. (2011). Crop yield forecasting by adaptive neuro fuzzy inference system. Mathematical Theory and Modeling, 1(3), 1-7.
 Mohammed, J. A. & Mahi, B. H. (2018). The Prediction of Solar Radiation using Fuzzy Logic: A Case Study. Journal of University of Duhok: Pure and Engineering Sciences, 21(2).
 Polykretis, C., Chalkias, C., & Ferentinou, M. (2019). Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area. Bulletin of Engineering Geology and the Environment, 78(2), 1173-1187.
 Rahimzadeh, A., Ashtiani, F. Z., & Okhovat, A. (2016). Application of adaptive neuro-fuzzy inference system as a reliable approach for prediction of oily wastewater microfiltration permeate volume. Journal of environmental chemical engineering, 4(1), 576-584
 Rao, N. S. (2006). Seasonal variation of groundwater quality in a part of Guntur District, Andhra Pradesh, India. Environmental Geology, 49(3), 413-429.
 Sahu, M., Mahapatra, S. S., Sahu, H. B., & Patel, R. K. (2011). Prediction of water quality index using neuro fuzzy inference system. Water Quality, Exposure and Health, 3(3-4), 175-191.
 Shabani, M. O., & Mazahery, A. (2012). Artificial intelligence in numerical modeling of nano sized ceramic particulates reinforced metal matrix composites. Applied Mathematical Modelling, 36(11), 5455-5465.
 Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, (1), 116-132.
 Talebizadeh, M., & Moridnejad, A. (2011). Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Systems with Applications, 38(4), 4126-4135.
 Tiwari, S., Babbar, R., & Kaur, G. (2018). Performance Evaluation of Two ANFIS Models for Predicting Water Quality Index of River Satluj (India). Advances in Civil Engineering, 2018.
 Tiwari, T. N., & Mishra, M. A. (1985). A preliminary assignment of water quality index of major Indian rivers. Indian J Environ Prot, 5(4), 276-279.
 Vernieuwe, H., Georgieva, O., De Baets, B., Pauwels, V. R., Verhoest, N. E., & De Troch, F. P. (2005). Comparison of data-driven Takagi–Sugeno models of rainfall–discharge dynamics. Journal of Hydrology, 302(1-4), 173-186.
 World Health Organization (WHO. (1996). WHO Guidelines For Drinking Water Quality 2nd Edition.
 World Health Organization. (WHO) (2017). Guidelines for drinking-water quality: first addendum to the fourth edition.
 Yadav, A. K., Khan, P., & Sharma, S. K. (2010). Water Quality Index Assessment ofGroundwater in Todaraisingh Tehsil of Rajasthan State, India-A Greener Approach. Journal of Chemistry, 7(S1), S428-S432.
 Yan, H., Zou, Z., & Wang, H. (2010). Adaptive Neuro Fuzzy Inference System for Classification of Water Quality Status. Journal of Environmental Sciences, 22(12), 1891-1896.
How to Cite
Pure and Engineering Sciences