{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T07:33:41Z","timestamp":1776584021915,"version":"3.51.2"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:00:00Z","timestamp":1571788800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T00:00:00Z","timestamp":1571788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s00521-019-04560-8","type":"journal-article","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T17:26:23Z","timestamp":1571851583000},"page":"8463-8473","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Neuro-fuzzy-wavelet hybrid approach to estimate the future trends of river water quality"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7589-7364","authenticated-orcid":false,"given":"Kulwinder Singh","family":"Parmar","sequence":"first","affiliation":[]},{"given":"Sidhu Jitendra Singh","family":"Makkhan","sequence":"additional","affiliation":[]},{"given":"Sachin","family":"Kaushal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,23]]},"reference":[{"key":"4560_CR1","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1016\/j.renene.2004.03.011","volume":"29","author":"H Aksoy","year":"2004","unstructured":"Aksoy H, Toprak ZF, Aytek A, \u00dcnal NE (2004) Stochastic generation of hourly mean wind speed data. Renewable Energy 29:2111\u20132131","journal-title":"Renewable Energy"},{"key":"4560_CR2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jhydrol.2010.06.033","volume":"390","author":"J Adamowski","year":"2010","unstructured":"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\u201391","journal-title":"J Hydrol"},{"key":"4560_CR3","first-page":"2686","volume":"23","author":"K Adamowski","year":"2009","unstructured":"Adamowski K, Prokoph A, Adamowski J (2009) Development of a new method of wavelet aided trend detection and estimation. Hydrol Process Spec Issue Can Geophys Union Hydrol Sect 23:2686\u20132696","journal-title":"Hydrol Process Spec Issue Can Geophys Union Hydrol Sect"},{"key":"4560_CR4","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/S0965-9978(99)00063-0","volume":"31","author":"L Bodri","year":"2000","unstructured":"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\u2013321","journal-title":"Adv Eng Softw"},{"key":"4560_CR5","doi-asserted-by":"crossref","first-page":"659","DOI":"10.5194\/angeo-23-659-2005","volume":"23","author":"Z Can","year":"2005","unstructured":"Can Z, Aslan Z, Oguz O, Siddiqi AH (2005) Wavelet transform of metrological parameter and gravity waves. Ann Geophys 23:659\u2013663","journal-title":"Ann Geophys"},{"key":"4560_CR6","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.advwatres.2010.03.007","volume":"33","author":"HW Chen","year":"2010","unstructured":"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\u2013666","journal-title":"Adv Water Resour"},{"key":"4560_CR7","unstructured":"CPCB, Water Quality Status of Yamuna River (1999\u20132005) (2006) Central Pollution Control Board, Ministry of Environment & Forests, Assessment and Development of River Basin Series: ADSORBS\/41\/2006-07"},{"key":"4560_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0022-1694(92)90046-X","volume":"137","author":"MN French","year":"1992","unstructured":"French MN, Krajewski WF, Cuykendall RR (1992) Rainfall forecasting in space and time using neural networks. J Hydrol 137:1\u201331","journal-title":"J Hydrol"},{"key":"4560_CR9","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/S0165-1684(97)00203-X","volume":"64","author":"D Furundzic","year":"1998","unstructured":"Furundzic D (1998) Application example of neural networks for time series analysis: rainfall-runoff modeling. Sig Process 64:383\u2013396","journal-title":"Sig Process"},{"key":"4560_CR10","volume-title":"Neural networks, a comprehensive foundation","author":"S Haykin","year":"1994","unstructured":"Haykin S (1994) Neural networks, a comprehensive foundation. Macmillan College Publishing Company, New York"},{"key":"4560_CR11","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1029\/95WR01955","volume":"31","author":"K Hsu","year":"1995","unstructured":"Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall runoff process. Water Resour Res 31:2517\u20132530","journal-title":"Water Resour Res"},{"key":"4560_CR12","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.5194\/hess-13-1413-2009","volume":"13","author":"NQ Hung","year":"2009","unstructured":"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\u20131425","journal-title":"Hydrol Earth Syst Sci"},{"key":"4560_CR13","first-page":"373","volume":"2","author":"P Jain","year":"2005","unstructured":"Jain P, Sharma JD, Sohu D, Sharma P (2005) Chemical analysis of drinking water of villages of Sanganer Tehsil, Jaipur District. Int J Environ Sci Technol 2:373\u2013379","journal-title":"Int J Environ Sci Technol"},{"key":"4560_CR14","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"JSR Jang","year":"1993","unstructured":"Jang JSR (1993) ANFIS: adaptive network based fuzzy inference system. IEEE Trans Syst Manag Cybern 23:665\u2013685","journal-title":"IEEE Trans Syst Manag Cybern"},{"key":"4560_CR15","doi-asserted-by":"crossref","first-page":"4467","DOI":"10.1007\/s11269-012-0157-3","volume":"26","author":"C Jeong","year":"2012","unstructured":"Jeong C, Shin JY, Kim T, Heo JH (2012) Monthly precipitation forecasting with a neuro-fuzzy model. Water Resour Manage 26:4467\u20134483","journal-title":"Water Resour Manage"},{"key":"4560_CR16","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.jhydrol.2003.11.006","volume":"289","author":"E Kahya","year":"2004","unstructured":"Kahya E, Kalayci S (2004) Trend analysis of streamflow in Turkey. J Hydrol 289:128\u2013144","journal-title":"J Hydrol"},{"issue":"Suppl 1","key":"4560_CR17","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s00521-013-1344-8","volume":"23","author":"A Kant","year":"2013","unstructured":"Kant A, Suman PK, Giri BK, Tiwari MK, Chatterjee C (2013) Comparison of multi-objective evolutionary neural network, adaptive neuro-fuzzy inference system and bootstrap- based neural network for flood forecasting. Neural Comput Appl 23(Suppl 1):231\u2013246","journal-title":"Neural Comput Appl"},{"issue":"7","key":"4560_CR18","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1016\/j.advwatres.2006.04.003","volume":"29","author":"S Karmakar","year":"2006","unstructured":"Karmakar S, Mujumdar PP (2006) Grey fuzzy optimization model for water quality management of a river system. Adv Water Resour 29(7):1088\u20131105","journal-title":"Adv Water Resour"},{"key":"4560_CR19","first-page":"683","volume":"50","author":"O Kisi","year":"2005","unstructured":"Kisi O (2005) Suspended sediment estimation using neuro fuzzy and neural network approaches. Hydrol Sci J 50:683\u2013696","journal-title":"Hydrol Sci J"},{"issue":"7","key":"4560_CR20","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s11869-017-0477-9","volume":"10","author":"O Kisi","year":"2017","unstructured":"Kisi O, Parmar KS, Soni K, Demir V (2017) Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models. Air Qual Atmos Health 10(7):873\u2013883","journal-title":"Air Qual Atmos Health"},{"key":"4560_CR21","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.jhydrol.2015.12.014","volume":"534","author":"O Kisi","year":"2016","unstructured":"Kisi O, Parmar KS (2016) Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. J Hydrol 534:104\u2013112","journal-title":"J Hydrol"},{"key":"4560_CR22","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1002\/hyp.1187","volume":"17","author":"M Lafreni\u00e8re","year":"2003","unstructured":"Lafreni\u00e8re M, Sharp M (2003) Wavelet analysis of inter-annual variability in the runoff regimes of glacial and nival stream catchments, Bow Lake, Alberta. Hydrol Process 17:1093\u20131118","journal-title":"Hydrol Process"},{"key":"4560_CR23","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jhydrol.2005.02.029","volume":"322","author":"NS Loboda","year":"2006","unstructured":"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\u201324","journal-title":"J Hydrol"},{"key":"4560_CR24","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1111\/j.1937-5956.2001.tb00076.x","volume":"10","author":"W Luk","year":"2001","unstructured":"Luk W, Fleischmann M, Beullens P, Bloemhof-Ruwaard JM (2001) The impact of product recovery on logistics network design. Prod Oper Manag 10:156\u2013173","journal-title":"Prod Oper Manag"},{"key":"4560_CR25","volume-title":"A wavelet tour of signal processing","author":"S Mallat","year":"2001","unstructured":"Mallat S (2001) A wavelet tour of signal processing, 2nd edn. Academic Press, San Diego","edition":"2"},{"key":"4560_CR26","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1007\/s11269-012-0239-2","volume":"27","author":"V Moosavi","year":"2013","unstructured":"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\u20131321","journal-title":"Water Resour Manage"},{"key":"4560_CR27","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1007\/s11269-011-9790-5","volume":"25","author":"KP Moustris","year":"2011","unstructured":"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\u20131993","journal-title":"Water Resour Manag"},{"key":"4560_CR28","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jhydrol.2003.12.010","volume":"291","author":"PC Nayak","year":"2004","unstructured":"Nayak PC, Sudheer KP, Ranjan DM, Ramasastri KS (2004) A neuro fuzzy computing technique for modeling hydrological time series. J Hydrol 291:52\u201366","journal-title":"J Hydrol"},{"key":"4560_CR29","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1007\/s00521-017-3155-9","volume":"31","author":"H Nozari","year":"2019","unstructured":"Nozari H, Azadi S (2019) Experimental evaluation of artificial neural network for predicting drainage water and groundwater salinity at various drain depths and spacing. Neural Comput Appl 31:1227\u20131236","journal-title":"Neural Comput Appl"},{"key":"4560_CR30","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.jhydrol.2007.05.026","volume":"342","author":"T Partal","year":"2007","unstructured":"Partal T, Kisi O (2007) Wavelet and neuro fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199\u2013212","journal-title":"J Hydrol"},{"key":"4560_CR31","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s13762-012-0086-y","volume":"10","author":"KS Parmar","year":"2013","unstructured":"Parmar KS, Bhardwaj R (2013) Water quality index and fractal dimension analysis of water parameters. Int J Environ Sci Technol 10:151\u2013164","journal-title":"Int J Environ Sci Technol"},{"key":"4560_CR32","first-page":"10172","volume":"219","author":"KS Parmar","year":"2013","unstructured":"Parmar KS, Bhardwaj R (2013) Wavelet and statistical analysis of river water quality parameters. Appl Math Comput 219:10172\u201310182","journal-title":"Appl Math Comput"},{"key":"4560_CR33","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s11269-014-0824-7","volume":"29","author":"KS Parmar","year":"2015","unstructured":"Parmar KS, Bhardwaj R (2015) River water prediction modeling using neural networks, fuzzy and wavelet coupled model. Water Resour Manage 29:17\u201333","journal-title":"Water Resour Manage"},{"key":"4560_CR34","first-page":"47","volume":"3","author":"BG Prasad","year":"2004","unstructured":"Prasad BG, Narayana TS (2004) Subsurface water quality of different sampling stations with some selected parameters at Machilipatnam Town. Nat Environ Pollut Technol 3:47\u201350","journal-title":"Nat Environ Pollut Technol"},{"key":"4560_CR35","doi-asserted-by":"crossref","first-page":"3539","DOI":"10.1007\/s11269-012-0089-y","volume":"26","author":"SC Pinto","year":"2012","unstructured":"Pinto SC, Adamowski J, Oron G (2012) Forecasting urban water demand via wavelet-denoising and neural network models. Case study: city of Syracuse, Italy. Water Resour Manage 26:3539\u20133558","journal-title":"Water Resour Manage"},{"key":"4560_CR36","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/S0022-1694(98)00273-X","volume":"216","author":"N Sajikumar","year":"1999","unstructured":"Sajikumar N, Thandaveswara BS (1999) A non-linear rainfall-runoff model using an artificial neural network. J Hydrol 216:32\u201355","journal-title":"J Hydrol"},{"key":"4560_CR37","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1080\/02626669909492272","volume":"44","author":"L See","year":"1999","unstructured":"See L, Openshaw S (1999) Applying soft computing approaches to river level forecasting. Hydrol Sci J 44:763\u2013777","journal-title":"Hydrol Sci J"},{"issue":"2","key":"4560_CR38","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s11269-013-0446-5","volume":"28","author":"RR Sahay","year":"2014","unstructured":"Sahay RR, Srivastava A (2014) Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network. Water Resour Manag 28(2):301\u2013317","journal-title":"Water Resour Manag"},{"issue":"9","key":"4560_CR39","doi-asserted-by":"crossref","first-page":"3507","DOI":"10.1007\/s11269-013-0361-9","volume":"27","author":"AA Seyed","year":"2013","unstructured":"Seyed AA, Ahmed E, Jaafar O (2013) Improving rainfall forecasting efficiency using modified adaptive neurofuzzy inference system (MANFIS). Water Resour Manag 27(9):3507\u20133523","journal-title":"Water Resour Manag"},{"key":"4560_CR40","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1007\/s00521-015-1871-6","volume":"26","author":"MSA Siddiquee","year":"2015","unstructured":"Siddiquee MSA, Hossain MMA (2015) Development of a sequential artificial neural network for predicting river water levels based on Brahmaputra and Ganges water levels. Neural Comput Appl 26:1979\u20131990","journal-title":"Neural Comput Appl"},{"key":"4560_CR41","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1007\/s11270-013-1832-6","volume":"225","author":"K Soni","year":"2014","unstructured":"Soni K, Kapoor S, Parmar KS (2014) Long-term aerosol characteristics over eastern, southeastern, and south coalfield regions in India. Water Air Soil Pollut 225:1832","journal-title":"Water Air Soil Pollut"},{"key":"4560_CR42","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.atmosres.2014.05.025","volume":"149","author":"K Soni","year":"2014","unstructured":"Soni K, Kapoor S, Parmar KS, Kaskaoutis DG (2014) Statistical analysis of aerosols over the Gangetic-Himalayan region using ARIMA model based on long-term MODIS observations. Atmos Res 149:174\u2013192","journal-title":"Atmos Res"},{"key":"4560_CR43","doi-asserted-by":"crossref","first-page":"3652","DOI":"10.1007\/s11356-014-3561-9","volume":"22","author":"K Soni","year":"2015","unstructured":"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\u20133671","journal-title":"Environ Sci Pollut Res"},{"key":"4560_CR44","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1007\/s40808-017-0366-0","volume":"3","author":"K Soni","year":"2017","unstructured":"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\u20131198","journal-title":"Model Earth Syst Environ"},{"key":"4560_CR45","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.1016\/j.watres.2003.08.004","volume":"38","author":"ZF Toprak","year":"2004","unstructured":"Toprak ZF, Sen Z, Savci ME (2004) Comment on Longitudinal dispersion coefficients in natural channels. Water Res 38:3139\u20133143","journal-title":"Water Res"},{"key":"4560_CR46","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1002\/clen.200800152","volume":"37","author":"ZF Toprak","year":"2009","unstructured":"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\u2013564","journal-title":"CLEAN Soil Air Water"},{"key":"4560_CR47","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1002\/clen.200900146","volume":"37","author":"ZF Toprak","year":"2009","unstructured":"Toprak ZF (2009) Flow discharge modeling in open canals using a new fuzzy modeling technique (SMRGT). CLEAN Soil Air Water 37:742\u2013752","journal-title":"CLEAN Soil Air Water"},{"key":"4560_CR48","volume-title":"Time series analysis","author":"WWS Wiee","year":"1990","unstructured":"Wiee WWS (1990) Time series analysis. Addision Wesley Publishing Company, New York"},{"key":"4560_CR49","first-page":"385","volume-title":"Vector autoregressive models for multivariate time series. Modelling financial time series with S-PLUS","author":"E Zivot","year":"2006","unstructured":"Zivot E, Wang J (2006) Vector autoregressive models for multivariate time series. Modelling financial time series with S-PLUS. Springer, New York, pp 385\u2013429"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04560-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04560-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04560-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T14:51:41Z","timestamp":1721919101000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04560-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,23]]},"references-count":49,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["4560"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04560-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,23]]},"assertion":[{"value":"6 June 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}