{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:24:37Z","timestamp":1778048677415,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,7,21]],"date-time":"2020-07-21T00:00:00Z","timestamp":1595289600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,21]],"date-time":"2020-07-21T00:00:00Z","timestamp":1595289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s00521-020-05164-3","type":"journal-article","created":{"date-parts":[[2020,7,21]],"date-time":"2020-07-21T18:09:17Z","timestamp":1595354957000},"page":"2853-2871","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data"],"prefix":"10.1007","volume":"33","author":[{"given":"Rana Muhammad","family":"Adnan","sequence":"first","affiliation":[]},{"given":"Zhongmin","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Kulwinder Singh","family":"Parmar","sequence":"additional","affiliation":[]},{"given":"Kirti","family":"Soni","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7847-5872","authenticated-orcid":false,"given":"Ozgur","family":"Kisi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,21]]},"reference":[{"issue":"5","key":"5164_CR1","first-page":"655","volume":"26","author":"H Wang","year":"2006","unstructured":"Wang H, Lau KM (2006) Atmospheric hydrological cycle in the tropics in twentieth century coupled climate simulations. Int J Climatol J Royal Meteorol Soc 26(5):655\u2013678","journal-title":"Int J Climatol J Royal Meteorol Soc"},{"issue":"1\u20134","key":"5164_CR2","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/S0022-1694(03)00267-1","volume":"283","author":"MH Costa","year":"2003","unstructured":"Costa MH, Botta A, Cardille JA (2003) Effects of large-scale changes in land cover on the discharge of the Tocantins River. Southeastern Amazonia. J Hydrol 283(1\u20134):206\u2013217","journal-title":"Southeastern Amazonia. J Hydrol"},{"issue":"4","key":"5164_CR3","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1029\/WR010i004p00763","volume":"10","author":"KP Singh","year":"1967","unstructured":"Singh KP, Lonnquist CG (1967) Two-distribution method for modeling and sequential generation of monthly streamflows. Water Resour Res 10(4):763\u2013773","journal-title":"Water Resour Res"},{"issue":"2","key":"5164_CR4","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/0169-2070(85)90022-6","volume":"1","author":"DJ Noakes","year":"1985","unstructured":"Noakes DJ, McLeod AI, Hipel KW (1985) Forecasting monthly riverflow time series. Int J Forecast 1(2):179\u2013190","journal-title":"Int J Forecast"},{"issue":"6","key":"5164_CR5","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1061\/(ASCE)0733-9496(1994)120:6(857)","volume":"120","author":"M Bender","year":"1994","unstructured":"Bender M, Simonovic S (1994) Time-series modeling for long-range stream-flow forecasting. J Water Resour Plan Manag 120(6):857\u2013870","journal-title":"J Water Resour Plan Manag"},{"key":"5164_CR6","volume-title":"Time series modeling of water resources and environmental systems","author":"KW Hipel","year":"1994","unstructured":"Hipel KW, McLeod AI (1994) Time series modeling of water resources and environmental systems. Elsevier, Amsterdam"},{"issue":"1","key":"5164_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4311\/jcks2007ES0017","volume":"72","author":"MR Ghanbarpour","year":"2007","unstructured":"Ghanbarpour MR, Abbaspour KC, Jalalvand G, Moghaddam GA (2007) Stochastic modelling of surface stream flow at different time scales: sangsoorakh karst basin, Iran. J Cave Karst Stud 72(1):1\u201310","journal-title":"J Cave Karst Stud"},{"key":"5164_CR8","unstructured":"Markus M, Salas JD, Shin H (1995) Predicting streamflows based on neural networks. In: Proceedings of the first international conference on water resources engineering, American Society of Civil Engineers, New York, pp 1641\u20131646"},{"issue":"4","key":"5164_CR9","doi-asserted-by":"publisher","first-page":"641","DOI":"10.5194\/hess-6-641-2002","volume":"6","author":"JC Ochoa-Rivera","year":"2002","unstructured":"Ochoa-Rivera JC, Garc\u00eda-Bartual R, Andreu J (2002) Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks. Hydrol Earth Syst Sci Discuss Eur Geosci Union 6(4):641\u2013654","journal-title":"Hydrol Earth Syst Sci Discuss Eur Geosci Union"},{"issue":"5","key":"5164_CR10","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1061\/(ASCE)1084-0699(2005)10:5(353)","volume":"10","author":"MS Mondal","year":"2005","unstructured":"Mondal MS, Wasimi SA (2005) Periodic transfer function-noise model for forecasting. J Hydrol Eng 10(5):353\u2013362. https:\/\/doi.org\/10.1061\/(ASCE)1084-0699(2005)10:5(353)","journal-title":"J Hydrol Eng"},{"key":"5164_CR11","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1080\/10286600600888565","volume":"24","author":"O Kisi","year":"2007","unstructured":"Kisi O, Cigizoglu HK (2007) Comparison of different ANN techniques in river flow prediction. Civ Eng Environ Syst 24:211\u2013231","journal-title":"Civ Eng Environ Syst"},{"key":"5164_CR12","unstructured":"Adnan RM, Yuan X, Kisi O, Yuan Y, Tayyab M, Lei X (2017) Application of soft computing models in streamflow forecasting. In: Proceedings of the Institution of Civil Engineers-Water Management, Thomas Telford Ltd, pp 1\u201312"},{"key":"5164_CR13","doi-asserted-by":"publisher","first-page":"4142","DOI":"10.1002\/hyp.7014","volume":"22","author":"O Kisi","year":"2008","unstructured":"Kisi O (2008) Stream flow forecasting using neuro-wavelet technique. Hydrol Process 22:4142\u20134152","journal-title":"Hydrol Process"},{"issue":"7","key":"5164_CR14","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.advengsoft.2008.08.002","volume":"40","author":"MC Demirel","year":"2009","unstructured":"Demirel MC, Venancio A, Kahya E (2009) Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Adv Eng Softw 40(7):467\u2013473","journal-title":"Adv Eng Softw"},{"issue":"7","key":"5164_CR15","doi-asserted-by":"publisher","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 K, Coskun HG, Andic G, Alganci U (2009) Modeling monthly mean flow in a poorly gauged basin by fuzzy logic. Clean Soil Air Water 37(7):555\u2013564","journal-title":"Clean Soil Air Water"},{"key":"5164_CR16","unstructured":"Rabenja AT, Ratiarison A, Rabeharisoa JM (2009) Forecasting of the rainfall and the discharge of the Namorona River in Vohiparara and FFT analyses of these data. In: Proceedings, 4th international conference in high-energy physics, Antananarivo, Madagascar, pp. 1\u201312"},{"key":"5164_CR17","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.jhydrol.2010.06.013","volume":"389","author":"O Kisi","year":"2010","unstructured":"Kisi O (2010) Wavelet regression model for short-term streamflow forecasting. J Hydrol 389:344\u2013353","journal-title":"J Hydrol"},{"issue":"8","key":"5164_CR18","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1007\/s00477-018-1560-y","volume":"32","author":"X Yuan","year":"2018","unstructured":"Yuan X, Chen C, Lei X, Yuan Y, Adnan RM (2018) Monthly runoff forecasting based on LSTM\u2013ALO model. Stoch Env Res Risk Assess 32(8):2199\u20132212","journal-title":"Stoch Env Res Risk Assess"},{"key":"5164_CR19","doi-asserted-by":"crossref","unstructured":"Pintelas E, Livieris IE, Stavroyiannis S, Kotsilieris T, Pintelas P (2020a) Investigating the problem of cryptocurrency price prediction: a deep learning approach. In: 16th international conference on artificial intelligence applications and innovations (AIAI 2020)","DOI":"10.1007\/978-3-030-49186-4_9"},{"key":"5164_CR20","unstructured":"Pintelas E, Livieris IE, Stavroyiannis S, Kotsilieris T, Pintelas P (2020b) Fundamental research questions and proposals on predicting cryptocurrency prices using DNNs, Technical Report TR20-01, University of Patras, Greece. (NIMERTIS) http:\/\/hdl.handle.net\/10889\/13296"},{"issue":"3","key":"5164_CR21","doi-asserted-by":"publisher","first-page":"269","DOI":"10.3882\/j.issn.16742370.2010.03.003","volume":"3","author":"S Abudu","year":"2010","unstructured":"Abudu S, Cui C-L, King JP, Abudukadeer K (2010) Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River. China Water Sci Eng 3(3):269\u2013281. https:\/\/doi.org\/10.3882\/j.issn.16742370.2010.03.003","journal-title":"China Water Sci Eng"},{"key":"5164_CR22","doi-asserted-by":"publisher","DOI":"10.1029\/2010WR010208","author":"Z Hao","year":"2011","unstructured":"Hao Z, Singh VP (2011) Single Site Monthly Streamflow Simulation using Entropy Theory. Water Resources Research. https:\/\/doi.org\/10.1029\/2010WR010208","journal-title":"Water Resources Research"},{"key":"5164_CR23","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1111\/j.1747-6593.2012.00337.x","volume":"26","author":"I Can","year":"2012","unstructured":"Can I, Tosunogulu F, Kahya E (2012) Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Coruh basin, Turkey. Water Environ J 26:567\u2013576","journal-title":"Water Environ J"},{"key":"5164_CR24","doi-asserted-by":"publisher","first-page":"929","DOI":"10.5194\/hess-22-929-2018","volume":"22","author":"S Schick","year":"2018","unstructured":"Schick S, R\u00f6ssler O, Weingartner R (2018) Monthly streamflow forecasting at varying spatial scales in the Rhine basin. Hydrol Earth Syst Sci 22:929\u2013942. https:\/\/doi.org\/10.5194\/hess-22-929-2018","journal-title":"Hydrol Earth Syst Sci"},{"issue":"11","key":"5164_CR25","doi-asserted-by":"publisher","first-page":"251","DOI":"10.14311\/NNW.2011.21.015","volume":"3","author":"R Samsudin","year":"2011","unstructured":"Samsudin R, Saad P, Shabri A (2011) A hybrid GMDH and least squares support vector machines in time series forecasting. Neural Netw World 3(11):251\u2013268","journal-title":"Neural Netw World"},{"issue":"6","key":"5164_CR26","doi-asserted-by":"publisher","first-page":"48","DOI":"10.5539\/mas.v9n6p48","volume":"9","author":"B Badyalina","year":"2015","unstructured":"Badyalina B, Shabri A (2015) Flood frequency analysis at ungauged site using group method of data handling and canonical correlation analysis. Mod Appl Sci 9(6):48","journal-title":"Mod Appl Sci"},{"key":"5164_CR27","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/978-981-10-8476-8_15","volume-title":"Big data in engineering applications","author":"O Kisi","year":"2018","unstructured":"Kisi O, Shiri J, Karimi S, Adnan RM (2018) Three different adaptive neuro fuzzy computing techniques for forecasting long-period daily streamflows\u2019. In: Roy SS et al (eds) Big data in engineering applications. Singapore, Springer, pp 303\u2013321"},{"issue":"14","key":"5164_CR28","doi-asserted-by":"publisher","first-page":"4469","DOI":"10.1007\/s11269-018-2033-2","volume":"32","author":"RM Adnan","year":"2018","unstructured":"Adnan RM, Yuan X, Kisi O, Adnan M, Mehmood A (2018) Stream flow forecasting of poorly gauged mountainous watershed by least square support vector machine, fuzzy genetic algorithm and M5 model tree using climatic data from nearby station. Water Resour Manag 32(14):4469\u20134486","journal-title":"Water Resour Manag"},{"key":"5164_CR29","doi-asserted-by":"publisher","first-page":"123981","DOI":"10.1016\/j.jhydrol.2019.123981","volume":"577","author":"RM Adnan","year":"2019","unstructured":"Adnan RM, Liang Z, Trajkovic S, Zounemat-Kermani M, Li B, Kisi O (2019) Daily streamflow prediction using optimally pruned extreme learning machine. J Hydrol 577:123981","journal-title":"J Hydrol"},{"issue":"3","key":"5164_CR30","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1007\/s11269-018-2178-z","volume":"33","author":"A Kumar","year":"2019","unstructured":"Kumar A, Kumar P, Singh VK (2019) Evaluating different machine learning models for runoff and suspended sediment simulation. Water Resour Manag 33(3):1217\u20131231","journal-title":"Water Resour Manag"},{"key":"5164_CR31","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.jhydrol.2018.10.064","volume":"568","author":"X Luo","year":"2019","unstructured":"Luo X, Yuan X, Zhu S, Zhanya X, Meng L, Peng J (2019) A hybrid support vector regression framework for streamflow forecast. J Hydrol 568:184\u2013193","journal-title":"J Hydrol"},{"issue":"4","key":"5164_CR32","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.2166\/nh.2017.076","volume":"49","author":"O Eray","year":"2018","unstructured":"Eray O, Mert C, Kisi K (2018) Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation. Hydrol Res 49(4):1221\u20131233","journal-title":"Hydrol Res"},{"key":"5164_CR33","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.ecocom.2015.12.003","volume":"25","author":"M Stehlik","year":"2016","unstructured":"Stehlik M, Dusek J, Kiselak J (2016) Missing chaos in global climate change data interpreting? Ecol Complex 25:531\u2013759","journal-title":"Ecol Complex"},{"key":"5164_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/aos\/1176347963","volume":"19","author":"JH Friedman","year":"1991","unstructured":"Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1\u2013141","journal-title":"Ann Stat"},{"key":"5164_CR35","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1177\/096228029500400303","volume":"4","author":"JH Friedman","year":"1995","unstructured":"Friedman JH, Roosen CB (1995) An introduction to multivariate adaptive regression splines. Stat Methods Med Res 4:197\u2013217","journal-title":"Stat Methods Med Res"},{"key":"5164_CR36","doi-asserted-by":"publisher","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":"5164_CR37","doi-asserted-by":"publisher","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 10:873\u2013883","journal-title":"Air Qual Atmos Health"},{"key":"5164_CR38","doi-asserted-by":"publisher","first-page":"124371","DOI":"10.1016\/j.jhydrol.2019.124371","volume":"586","author":"RM Adnan","year":"2020","unstructured":"Adnan RM, Liang Z, Heddam S, Zounemat-Kermani M, Kisi O, Li B (2020) Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. J Hydrol 586:124371","journal-title":"J Hydrol"},{"key":"5164_CR39","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1109\/TSMC.1971.4308320","volume":"4","author":"AG Ivakhnenko","year":"1971","unstructured":"Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern 4:364\u2013378","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"5164_CR40","first-page":"187","volume":"10","author":"AG Ivakhnenko","year":"2000","unstructured":"Ivakhnenko AG, Ivakhnenko GA (2000) Problems of further development of the group method of data handling algorithms. Part I. Pattern Recognit Image Anal 10:187\u2013194","journal-title":"Part I. Pattern Recognit Image Anal"},{"key":"5164_CR41","doi-asserted-by":"publisher","first-page":"2588","DOI":"10.1016\/j.enconman.2008.05.025","volume":"49","author":"N Amanifard","year":"2008","unstructured":"Amanifard N, Nariman-Zadeh N, Farahani MH, Khalkhali A (2008) Modelling of multiple short-length-scale stall cells in an axial compressor using evolved gmdh neural networks. Energy Convers Manag 49:2588\u20132594","journal-title":"Energy Convers Manag"},{"key":"5164_CR42","doi-asserted-by":"publisher","DOI":"10.1142\/2896","volume-title":"Genetic algorithms and fuzzy logic systems: soft computing perspectives","author":"E Sanchez","year":"1997","unstructured":"Sanchez E, Shibata T, Zadeh LA (1997) Genetic algorithms and fuzzy logic systems: soft computing perspectives, vol 7. World Scientific, Singapore"},{"key":"5164_CR43","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1109\/91.995117","volume":"10","author":"NK Kasabov","year":"2002","unstructured":"Kasabov NK, Song Q (2002) DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10:144\u2013154","journal-title":"IEEE Trans Fuzzy Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05164-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05164-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05164-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T23:48:49Z","timestamp":1626824929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05164-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,21]]},"references-count":43,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5164"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05164-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,21]]},"assertion":[{"value":"17 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2020","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":"There is no conflict of interest in the presented study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}