Abstract
Water is the most important substance for life on earth and every living being need freshwater to survive. Besides various sources of water, river water is all-important source of freshwater. Due to rapid urbanization, industrialization, religious and social practices on the banks of rivers, the river water gets polluted and it is one of the major issues in India. So, the need of hour is to keep a continuous check on the quality of river water parameters. Various researchers have developed accurate prediction models to estimate the future quality of river water with least forecasting errors. Autoregressive time series models have been developed to generate linear forecast only and most of them are unable to handle nonlinear problems. To handle such nonlinear problems, artificial neural network (ANN) and adaptive neuro-fuzzy interface system are found to be most efficient tool for accurate prediction. Besides these methods, wavelet decomposition tool for analyzing nonlinear situations has been used to generate forecast values close enough to observed values. The biochemical oxygen (BOD) of river Yamuna at sample site of Nizamuddin (Delhi) is predicted using the past monthly averaged data. Statistical analysis provides basis to understand the nature of wavelet domain constitutive series. The prediction results obtained using neuro-fuzzy-wavelet coupled model generates more accurate outcomes as compared to neuro-fuzzy, ANN and regression models.
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References
Adamowski J, Sun K (2010) Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J Hydrol 390:85–91
Aksoy H, Toprak ZF, Aytek A, Ünal NE (2004) Stochastic generation of hourly mean wind speed data. Renew Energy 29:2111–2131
Bodri L, Cermak V (2000) Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia. Adv Eng Softw 31:311–321
Can Z, Aslan Z, Oguz O, Siddiqi AH (2005) Wavelet transform of metrological parameter and gravity waves. Ann Geophys 23:659–663
Chen HW, Chang NB (2010) Using fuzzy operators to address the complexity in decision making of water resources redistribution in two neighboring river basins. Adv Water Resour 33:652–666
Daubechies I (1992) Ten lectures on wavelets. SIAM, Philadelphia, PA
French MN, Krajewski WF, Cuykendall RR (1992) Rainfall forecasting in space and time using neural networks. J Hydrol 137:1–31
Furundzic D (1998) Application example of neural networks for time series analysis: rainfall-runoff modeling. Signal Process 64:383–396
Haykin S (1994) Neural networks, a comprehensive foundation. Macmillan College Publishing Company, New York
Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall runoff process. Water Resour Res 31:2517–2530
Hung NQ, Babel MS, Weesakul S, Tripathi NK (2009) An artificial neural network model for rainfall forecasting in Bangkok Thailand. Hydrol Earth Syst Sci 13:1413–1425
Jeong C, Shin JY, Kim T, Heo JH (2012) Monthly precipitation forecasting with a neuro-fuzzy model. Water Resour Manage 26:4467–4483
Kahya E, Kalayci S (2004) Trend analysis of streamflow in Turkey. J Hydrol 289:128–144
Karmakar S, Mujumdar PP (2006) Grey fuzzy optimization model for water quality management of a river system. Adv Water Resour 29(7):1088–1105
Kisi O (2005) Suspended sediment estimation using neuro fuzzy and neural network approaches. Hydrol Science Journal 50:683–696
Lafrenière M, Sharp M (2003) Wavelet analysis of inter-annual variability in the runoff regimes of glacial and nival stream catchments, Bow Lake, Alberta. Hydrolog Process 17:1093–1118
Loboda NS, Glushkov AV, Knokhlov VN, Lovett L (2006) Using non decimated wavelet decomposition to analyse time variations of North Atlantic Oscillation, eddy kinetic energy, and Ukrainian precipitation. J Hydrol 322:14–24
Luk W, Fleischmann M, Beullens P, Bloemhof-Ruwaard JM (2001) The impact of product recovery on logistics network design. Prod Oper Manage 10:156–173
Mallat S (2001) A wavelet tour of signal processing, 2nd edn. Academic Press, San Diego
Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour Manage 27:1301–1321
Moustris KP, Larissi IK, Nastos PT, Paliatsos AG (2011) Precipitation forecast using artificial neural networks in specific regions of Greece. Water Resour Manag 25:1979–1993
Nayak PC, Sudheer KP, Ranjan DM, Ramasastri KS (2004) A neuro fuzzy computing technique for modeling hydrological time series. J Hydrol 291:52–66
Partal T, Kisi O (2007) Wavelet and neuro fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199–212
Parmar KS, Chugh P, Minhas P, Sahota HS (2009) Alarming pollution levels in rivers of Punjab. Indian J Env Prot 29:953–959
Pinto SC, Adamowski J, Oron G (2012) Forecasting urbanwater demand viawavelet-denoising and neural network models. Case study: city of Syracuse, Italy. Water Resour Manage 26:3539–3558
Sahay RR, Srivastava A (2014) Predicting monsoon floods in rivers embedding wavelet transform, geneticalgorithm and neural network. Water Resour Manag 28(2):301–317
Sajikumar N, Thandaveswara BS (1999) A non-linear rainfall-runoff model using an artificial neural network. J Hydrol 216:32–55
See L, Openshaw S (1999) Applying soft computing approaches to river level forecasting. Hydrolog Sci J 44:763–777
Seyed AA, Ahmed E, Jaafar O (2013) Improving rainfall forecasting efficiency using modified adaptive neurofuzzy inference system (MANFIS). Water Resour Manag 27(9):3507–3523
Soni K, Kapoor S, Parmar KS (2014a) Long-term aerosol characteristics over eastern, southeastern, and south coalfield regions in India. Water Air Soil Pollut 225:1832
Soni K, Kapoor S, Parmar KS, Kaskaoutis DG (2014b) Statistical analysis of aerosols over the Gangetic-Himalayan region using ARIMA model based on long-term MODIS observations. Atmos Res 149:174–192
Soni K, Parmar KS, Kapoor S (2015) Time series model prediction and trend variability of aerosol optical depth over coal mines in India. Environ Sci Pollut Res 22:3652–3671
Soni K, Parmar KS, Agarwal S (2017) Modeling of air pollution in residential and industrial sites by integrating statistical and Daubechies Wavelet (Level 5) analysis. Model Earth Syst Environ 3:1187–1198
Toprak ZF (2009) Flow discharge modeling in open canals using a new fuzzy modeling technique (SMRGT). CLEAN-Soil Air Water 37:742–752
Toprak ZF, Sen Z, Savci ME (2004) Comment on Longitudinal dispersion coefficients in natural channels. Water Res 38:3139–3143
Toprak ZF, Eris E, Agiralioglu N, Cigizoglu HK, Yilmaz L, Aksoy H, Coskun G, Andic G, Alganci U (2009) Modeling monthly mean flow in a poorly gauged basin by fuzzy logic, CLEAN-soil, air. Water 37:555–564
Wiee WWS (1990) Time series analysis. Addision Wesley Publishing Company, New York, 478p
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Parmar, K.S., Soni, K., Singh, S. (2021). Prediction of River Water Quality Parameters Using Soft Computing Techniques. In: Deo, R., Samui, P., Kisi, O., Yaseen, Z. (eds) Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5772-9_20
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