Water Quality Classification Based on Minimum Qualitative Parameter (Case Study: Karun River)

Document Type : Research Paper

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Abstract

Rivers are considered one of the most important resources of providing fresh water. Restrictions faced by such resources underline the necessity of preserving their quality. Water quality indices are usually resorted to qualitatively monitor water resources. Any of such indices is calculated with regard to a series of specific qualitative parameters. The process of sampling and quantification of the aforementioned parameters are, however, time-consuming and costly. Finding a reliable method comprised of minimum qualitative parameters could be, therefore, of great help in classifying water quality. As an alternative to the common NSFQWI, the advantages of the Probabilistic Neural Network (PNN) as a classifier are used in the present study to classify water quality of Karun River. In order to fulfill this objective, the qualitative statistics of 172 samples were used in a way that qualitative parameters and water quality classes derived from NSFWQI are used respectively as the input and output of the model. The assessment criteria of error rate, error value, accuracy and Spearman’s correlation coefficient were used to evaluate the performance of PNN model. The results showed that through making use of merely three parameters of turbidity, fecal coliform and total dissolved solids, PNN model is capable of classifying water quality with the accuracy of 94.365% and 90.7769% at two stages of training and test respectively which in turn indicates considerable accuracy of PNN in determining water quality classification.

Keywords

Abbasi, T., Abbasi, S.A., 2012. Water quality indices. Elsevier, 375 p.
Asgari, H., Kerachian, R., 2007. Application of probability estimation of the support vector machines inwater quality classification of rivers (Case study: Karoon-dez river). 23-24 Jan., Isfahan University of Technology, Esfahan, Iran. (In Persian)
Balabin, R.M., Safieva, R.Z., Lomakina, E.I., 2010. Gasoline classification using near infrared (NIR) spectroscopy data: comparison of multivariate techniques. Analytica Chimica Acta 671(1), 27-35.
Brown, R.M., McClelland, N.I., Deininger, R.A., Tozer, R.G., 1970. A water quality index- do we dare. Water and Sewage Works, 339-343.
Brown,‎ R.M.,‎ McClelland,‎ N.I.,‎ Deininger,‎ R.A.,‎ O’Connor,‎ M.F.,‎ 1972.‎ A‎ water‎ quality‎ index—crashing the psychological barrier. In: Indicators of environmental quality. Springer, US, pp. 173-182.
Cacoullos, T., 1966. Estimation of a multivariate density. Annals of the Institute of Statistical Mathematics 18(1), 179-189.
Chen, A.S., Leung, M.T., Daouk, H., 2003. Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research 30(6), 901-923.
Horton, R.K., 1965. An index number system for rating water quality. Journal of Water Pollution Control Federation 37(3), 300-306.
Khaki, M., Yusoff, I., Islami, N., 2015. Application of the Artificial Neural Network and Neuro‐fuzzy System for Assessment of Groundwater Quality. CLEAN–Soil, Air, Water 43(4), 551-560.
Khashei, M., Bijari, M. Mokhatab Rafeiei, F., 2013. Exchange rate forecasting using Hybrid modeles of multi-layer perceptrons (MLPs) and probabilistic neural network (PNNs). Journal of Numerical Modeling in Engineering 32(1), 97- 113. (In Persian)
Kim, D.K., Lee, J.J., Lee, J.H., Chang, S.K., 2005. Application of probabilistic neural networks for prediction of concrete strength. Journal of Materials in Civil Engineering 17(3), 353-362.
Landwehr, J.M., Deininger, R.A., 1976. A comparison of several water quality indexes. Journal (Water Pollution Control Federation), 954-958.
Mao, K.Z., Tan, K.C., Ser, W., 2000. Probabilistic neural-network structure determination for pattern classification. Neural Networks, IEEE Transactions on 11(4), 1009-1016.
Masters, T., 1995. Advanced algorithms for neural networks: a C++ sourcebook. John Wiley & Sons. Modaresi, F., Araghinejad, S., 2014. A Comparative assessment of support vector machines, probabilistic neural networks, and K-Nearest neighbor algorithms for water quality classification. Water resources management 28(12), 4095-4111.
Rahman, M., Das, R., Hassan, N., Roy, K., Haque, F., Akber, A., 2014. Environmental study on water quality of Mayur River with reference to suitability for irrigation. International Journal of Environmental Sciences 5(2), 291-308.
Rasinezami, S., Nazariha, M., Baghvand, A., Moridi, A., 2012. Karkheh river water quality using multivariate statistical analysis and qualitative data variations. Journal of health system research 8(7), 1280-1292. (In Persian)
Rumpf, T., Mahlein, A.K., Steiner, U., Oerke, E.C., Dehne, H.W., Plümer, L., 2010. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture 74(1), 91-99.
Sakizadeh, M., 2015. Assessment the performance of classification methods in water quality studies, a case study in Karaj River. Environmental monitoring and assessment 187(9), 1-12.
Sakizadeh, M., Mirzaei, R., 2016. A comparative study of the performance of K-nearest neighbors and support vector machines for classification of groundwater. Journal of Mining and Environment 7(2), 149-164.
Sattari, M., Abbasgoli Naebzad, M., Mirabbasi Najafabadi, R., 2014. Surface water quality prediction using decison tree method. Journal of irrigation and water engineering 4(15), 76-88. (In Persian)
Singh, K.P., Basant, N., Gupta, S., 2011. Support vector machines in water quality management. Analytica chimica acta 703(2), 152-162. Specht, D.F., 1990. Probabilistic neural networks. Neural networks 3(1), 109-118.
Towler, E., Rajagopalan, B., Seidel, C., Summers, R.S., 2009. Simulating ensembles of source water quality using a Knearest neighbor resampling approach. Environmental science & technology 43(5), 1407-1411.
Walker,‎ D.,‎ Jakovljević, D.,‎ Savić,‎ D.,‎Radovanović,‎M.,‎ 2015.‎Multi-criterion water quality analysis of the Danube River in Serbia: A visualisation approach. Water research 79, 158-172.
Wasserman, P.D., 1993. Advanced methods in neural computing. Van Nostrand Reinhold, New York.
Xue, C.X., Zhang, X.Y., Liu, M.C., Hu, Z.D., Fan, B.T., 2005. Study of probabilistic neural networks to classify the active compounds in medicinal plants. Journal of pharmaceutical and biomedical analysis 38(3), 497-507.
Yang, Y., Ge, S.S., Lee, T.H., 2008. Hand gesture recognition and tracking based on distributed locally linear embedding. Image and Vision Computing 26(12), 1607-1620.