Abstract
This work focuses on the correlations among water quality parameters such as total hardness, total dissolved solids, electrical conductivity and total alkalinity with water parameters pH, temperature and the sum of mill equivalents of cations and anions in water reservoir in Kermanshah province (located in the middle of the western part of Iran). The data of water quality of monitoring sites were collected over year 2015. To predict and simulate water quality parameters, two data-driven models, i.e., adaptive neural fuzzy inference system and a hybrid adaptive neural fuzzy inference system structure trained by particle swarm optimization technique, were used. The main advantages of these methods are their high accuracy and very fast computational speed to predict unknown data. The results indicated that implementation of two models is highly satisfactory for predicting inorganic indicators of water quality. However, the flexibility of particle swarm optimization–adaptive neural fuzzy inference system method in modeling is better than adaptive neural fuzzy inference system approach. To prove this, the correlation coefficient, mean absolute error, root mean square error and t statistics were calculated as the error criterion. The overall (training and testing) mean relative error percentage, mean absolute error, root mean square error, correlation coefficient and t statistics obtained by the proposed particle swarm optimization–adaptive neural fuzzy inference system model are less than 3.50, 11.60, 18.90, 0.95 and 0.38%, respectively. The results provide a useful approach that uses water parameters to estimate water quality in water reservoir for water treatment and pollution management.
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References
Afshar A, Shafii M, Haddad OB (2011) Optimizing multi-reservoir operation rules: an improved HBMO approach. J Hydroinform 13:121–139
Aras E, Toğan V, Berkun M (2007) River water quality management model using genetic algorithm. Environ Fluid Mech 7:439–450
Bashir ZA, El-Hawary ME (2009) Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE T Power Syst 24:20–27
Behmel S, Damour M, Ludwig R, Rodriguez MJ (2016) Water quality monitoring strategies—a review and future perspectives. Sci Total Environ 571:1312–1329
Borjian H (2017) Kermanshah i. geography, Encyclopædia Iranica, XVI/3, pp. 316–319
Chen WB, Liu WC (2015) Water quality modeling in reservoirs using multivariate linear regression and two neural network models. Adv Artif Neur Syst 2015:521721
Delpla I, Benmarhnia T, Lebel A, Levallois P, Rodriguez MJ (2015) Investigating social inequalities in exposure to drinking water contaminants in rural areas. Environ Pollut 207:88–96
Erle E, Pontius R (2007) Land-use and land-cover change. Encyclopedia of Earth (eds) Cutler J. Cleveland (Washington, DC: Environmental Information Coalition, National Council for Science and the Environment), Last Retrieved January, 19, 2008
Fallah-Mehdipour E, Haddad OB, Mariño MA (2013) Developing reservoir operational decision rule by genetic programming. J Hydroinform 15:103–119
Gergel SE, Turner MG, Miller JR, Melack JM, Stanley EH (2002) Landscape indicators of human impacts to riverine systems. Aquat Sci 64:118–128
Haddad OB, Moradi-Jalal M, Mariño MA (2011) Design–operation optimisation of run-of-river power plants. P I Civil Eng Wat Manag 164:463–475
Jang JS, Sun CT (1995) Neuro-fuzzy modeling and control. P IEEE 83:378–406
Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence
Jones AS, Stevens DK, Horsburgh JS, Mesner NO (2011) Surrogate measures for providing high frequency estimates of total suspended solids and total phosphorus concentrations. J Am Wat Resour Assoc 47:239–253
Jonnalagadda SB, Mhere G (2001) Water quality of the Odzi River in the eastern highlands of Zimbabwe. Wat Res 35:2371–2376
Kang JH, Lee SW, Cho KH, Ki SJ, Cha SM, Kim JH (2010) Linking land-use type and stream water quality using spatial data of fecal indicator bacteria and heavy metals in the Yeongsan river basin. Wat Res 44:4143–4157
Kennedy J, and Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks
Kisi O, Shiri J (2011) Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Wat Resour Manag 25:3135–3152
Lenat DR, Crawford JK (1994) Effects of land use on water quality and aquatic biota of three North Carolina Piedmont streams. Hydrobiologia 294:185–199
Liu WC, Chung CE (2014) Enhancing the predicting accuracy of the water stage using a physical-based model and an artificial neural network-genetic algorithm in a river system. Wat 6:1642–1661
Miranda J, Krishnakumar G (2015) Microalgal diversity in relation to the physicochemical parameters of some Industrial sites in Mangalore South India. Environ Monit Assess 187:664
Mousavi SJ, Ponnambalam K, Karray F (2007) Inferring operating rules for reservoir operations using fuzzy regression and ANFIS. Fuzzy Set Syst 158:1064–1082
Naddafi K, Honari H, Ahmadi M (2007) Water quality trend analysis for the Karoon River in Iran. Environ Monit Assess 134:305–312
Najah A, El-Shafie A, Karim OA, Jaafar O, El-Shafie AH (2011) An application of different artificial intelligences techniques for water quality prediction. Int J Phys Sci 6:5298–5308
Ngoye E, Machiwa JF (2004) The influence of land-use patterns in the Ruvu river watershed on water quality in the river system. Phys Chem Earth 29:1161–1166
Salerno F, Viviano G, Carraro E, Manfredi EC, Lami A, Musazzi S, Marchetto A, Guyennon N, Tartari G, Copetti D (2014) Total phosphorus reference condition for subalpine lakes: a comparison among traditional methods and a new process-based watershed approach. J Environ Manag 145:94–105
Sibanda T, Chigor VN, Koba S, Obi CL, Okoh AI (2014) Characterisation of the physicochemical qualities of a typical rural-based river: ecological and public health implications. Int J Environ Sci Technol 11:1771–1780
Suen JP, Eheart JW (2003) Evaluation of neural networks for modeling nitrate concentrations in rivers. J Water Resour Plan Manag 129:505–510
Thompson MY, Brandes D, Kney AD (2012) Using electronic conductivity and hardness data for rapid assessment of stream water quality. J Environ Manag 104:152–157
Viviano G, Salerno F, Manfredi EC, Polesello S, Valsecchi S, Tartari G (2014) Surrogate measures for providing high frequency estimates of total phosphorus concentrations in urban watersheds. Wat Res 64:265–277
Woli KP, Nagumo T, Kuramochi K, Hatano R (2004) Evaluating river water quality through land use analysis and N budget approaches in livestock farming areas. Sci Total Environ 329:61–74
World Health Organization (1996) Guidelines for drinking-water quality, vol 2, 2nd edn. WHO, Geneva, p 991
World Health Organization (2004) Guidelines for drinking-water quality, vol 1, 3rd edn. WHO, Geneva, pp 143–220
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The authors would like to acknowledge the financial support of Rural Water and Sewage Company of Kermanshah Province for this research under Grant No. 94/8012.
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Editorial responsibility: M. Abbaspour.
The authors wish to express their thanks to rural water and Sewage Company of Kermanshah Province for their sincere help throughout this study.
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Aghel, B., Rezaei, A. & Mohadesi, M. Modeling and prediction of water quality parameters using a hybrid particle swarm optimization–neural fuzzy approach. Int. J. Environ. Sci. Technol. 16, 4823–4832 (2019). https://doi.org/10.1007/s13762-018-1896-3
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DOI: https://doi.org/10.1007/s13762-018-1896-3