{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T14:24:22Z","timestamp":1780583062367,"version":"3.54.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2013,8,13]],"date-time":"2013-08-13T00:00:00Z","timestamp":1376352000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2014,7]]},"DOI":"10.1007\/s00521-013-1469-9","type":"journal-article","created":{"date-parts":[[2013,8,12]],"date-time":"2013-08-12T05:37:03Z","timestamp":1376285823000},"page":"179-188","source":"Crossref","is-referenced-by-count":48,"title":["Forecasting of monthly river flow with autoregressive modeling and data-driven techniques"],"prefix":"10.1007","volume":"25","author":[{"given":"\u00d6zlem","family":"Terzi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00fcl\u015fah","family":"Ergin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2013,8,13]]},"reference":[{"key":"1469_CR1","volume-title":"Times series analysis forecasting and control","author":"GEP Box","year":"1970","unstructured":"Box GEP, Jenkins GM (1970) Times series analysis forecasting and control. Holden-Day, San Francisco"},{"key":"1469_CR2","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.jhydrol.2005.03.034","volume":"314","author":"CL Chang","year":"2005","unstructured":"Chang CL, Lo SL, Yu SL (2005) Applying fuzzy theory and genetic algorithm to interpolate precipitation. J Hydrol 314:92\u2013104","journal-title":"J Hydrol"},{"key":"1469_CR3","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1002\/hyp.226","volume":"15","author":"L-C Chang","year":"2001","unstructured":"Chang L-C, Chang F-J (2001) Intelligent control for modelling of real-time reservoir operation. Hydrol Process 15:1621\u20131634","journal-title":"Hydrol Process"},{"key":"1469_CR4","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/S0022-1694(02)00122-1","volume":"268","author":"CT Cheng","year":"2002","unstructured":"Cheng CT, Ou CP, Chau KW (2002) Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall\u2013runoff model calibration. J Hydrol 268:72\u201386","journal-title":"J Hydrol"},{"key":"1469_CR5","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.jhydrol.2006.10.021","volume":"334","author":"YM Chiang","year":"2007","unstructured":"Chiang YM, Chang FJ, Jou BJD, Lin PF (2007) Dynamic ANN for precipitation estimation and forecasting from radar observations. J Hydrol 334:250\u2013261","journal-title":"J Hydrol"},{"issue":"1","key":"1469_CR6","first-page":"129","volume":"20","author":"M \u00c7obaner","year":"2005","unstructured":"\u00c7obaner M, \u00c7etin M, Yurtal R (2005) Investigation of the deterministic and stochastic properties of the river flows. \u00c7ukurova Univ J Eng Archit Fac 20(1):129\u2013138 (In Turkish)","journal-title":"\u00c7ukurova Univ J Eng Archit Fac"},{"key":"1469_CR7","unstructured":"Demuth H, Beale M (2001) Neural network toolbox user\u2019s guide\u2014version 4. The Mathworks Inc, Natick, USA, pp 840"},{"key":"1469_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S1464-1909(01)85005-X","volume":"26","author":"YB Dibike","year":"2001","unstructured":"Dibike YB, Solomatine DP (2001) River flow forecasting using artificial neural networks. Phys Chem Earth B 26:1\u20137","journal-title":"Phys Chem Earth B"},{"key":"1469_CR9","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0306-2619(02)00016-8","volume":"71","author":"ASS Dorvlo","year":"2002","unstructured":"Dorvlo ASS, Jervase JA, Al-Lawati A (2002) Solar radiation estimation using artificial neural networks. Appl Energy 71:307\u2013319","journal-title":"Appl Energy"},{"issue":"2","key":"1469_CR10","first-page":"87","volume":"13","author":"C Ferreira","year":"2001","unstructured":"Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87\u2013129","journal-title":"Complex Syst"},{"key":"1469_CR11","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/978-1-4471-0123-9_54","volume-title":"Soft computing and \u0131ndustry-recent applications","author":"C Ferreira","year":"2002","unstructured":"Ferreira C (2002) Gene expression programming in problem solving. In: Roy R, Ovaska S, Furuhashi T, Hoffman F (eds) Soft computing and \u0131ndustry-recent applications. Springer, Berlin, pp 635\u2013654"},{"key":"1469_CR12","volume-title":"Gene-expression programming: Mathematical modeling by an artificial intelligence","author":"C Ferreira","year":"2006","unstructured":"Ferreira C (2006) Gene-expression programming: Mathematical modeling by an artificial intelligence. Springer, Berlin"},{"key":"1469_CR13","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.cageo.2009.09.014","volume":"36","author":"MA Ghorbani","year":"2010","unstructured":"Ghorbani MA, Khatibi R, Aytek A, Makarynskyy O, Shiri J (2010) Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks. Comput Geosci 36:620\u2013627","journal-title":"Comput Geosci"},{"issue":"2","key":"1469_CR14","first-page":"7","volume":"4","author":"C Grosan","year":"2006","unstructured":"Grosan C, Abraham A (2006) Evolving computer programs for knowledge discovery. Int J Syst Manag 4(2):7\u201324","journal-title":"Int J Syst Manag"},{"issue":"8","key":"1469_CR15","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1061\/(ASCE)HE.1943-5584.0000044","volume":"14","author":"A G\u00fcven","year":"2009","unstructured":"G\u00fcven A, Aytek A (2009) New approach for stage\u2013discharge relationship: gene-expression programming. J Hydrol Eng 14(8):812\u2013820","journal-title":"J Hydrol Eng"},{"key":"1469_CR16","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/S0022-1694(00)00228-6","volume":"233","author":"CE Imrie","year":"2000","unstructured":"Imrie CE, Durucan S, Korre A (2000) River flow prediction using artificial neural networks: generalization beyond the calibration range. J Hydrol 233:138\u2013153","journal-title":"J Hydrol"},{"issue":"5","key":"1469_CR17","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/72.159060","volume":"3","author":"JSR Jang","year":"1992","unstructured":"Jang JSR (1992) Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans Neural Networks 3(5):714\u2013723","journal-title":"IEEE Trans Neural Networks"},{"issue":"7","key":"1469_CR18","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1061\/(ASCE)HE.1943-5584.0000051","volume":"14","author":"ME Keskin","year":"2009","unstructured":"Keskin ME, Taylan ED (2009) Artificial models for inter-basin flow prediction in southern Turkey. J Hydrol Eng 14(7):752\u2013758","journal-title":"J Hydrol Eng"},{"issue":"1","key":"1469_CR19","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1061\/(ASCE)1084-0699(2006)11:1(65)","volume":"11","author":"ME Keskin","year":"2006","unstructured":"Keskin ME, Terzi \u00d6 (2006) Artificial neural network models of daily pan evaporation. J Hydrol Eng 11(1):65\u201370","journal-title":"J Hydrol Eng"},{"issue":"1","key":"1469_CR20","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1061\/(ASCE)1084-0699(2004)9:1(60)","volume":"9","author":"\u00d6 Ki\u015fi","year":"2004","unstructured":"Ki\u015fi \u00d6 (2004) River flow modeling using artificial neural networks. J Hydrol Eng 9(1):60\u201363","journal-title":"J Hydrol Eng"},{"key":"1469_CR21","volume-title":"Genetic programming on the programming of computers by means of natural selection. A Bradford book","author":"JR Koza","year":"1992","unstructured":"Koza JR (1992) Genetic programming on the programming of computers by means of natural selection. A Bradford book. The MIT Press, Cambridge"},{"key":"1469_CR22","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1023\/B:WARM.0000024727.94701.12","volume":"18","author":"DN Kumar","year":"2004","unstructured":"Kumar DN, Raju KS, Sathish T (2004) River flow forecasting using recurrent neural networks. Water Resour Manag 18:143\u2013161","journal-title":"Water Resour Manag"},{"key":"1469_CR23","volume-title":"Neural networks in computer intelligence","author":"L Fu","year":"1994","unstructured":"Fu L (1994) Neural networks in computer intelligence. McGraw-Hill International Editions, New York"},{"key":"1469_CR24","unstructured":"Lin CT, Lee CSG (1995) Neural fuzzy systems. Prentice Hall PTR 797, NJ"},{"key":"1469_CR25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jhydrol.2003.10.015","volume":"289","author":"GF Lin","year":"2004","unstructured":"Lin GF, Chen LH (2004) A non-linear rainfall-runoff model using radial basis function network. J Hydrol 289:1\u20138","journal-title":"J Hydrol"},{"key":"1469_CR26","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.jhydrol.2009.06.047","volume":"375","author":"GF Lin","year":"2009","unstructured":"Lin GF, Wu MC (2009) A hybrid neural network model for typhoon-rainfall forecasting. J Hydrol 375:450\u2013458","journal-title":"J Hydrol"},{"key":"1469_CR27","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.jhydrol.2008.01.023","volume":"352","author":"A Makkeasorn","year":"2008","unstructured":"Makkeasorn A, Chang NB, Zhou X (2008) Short-term streamflow forecasting with global climate change implications\u2014a comparative study between genetic programming and neural network models. J Hydrol 352:336\u2013354","journal-title":"J Hydrol"},{"key":"1469_CR28","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/S0960-1481(98)00001-9","volume":"13","author":"M Mohandes","year":"1998","unstructured":"Mohandes M, Rehman S, Halawani TO (1998) A neural networks approach for wind speed prediction. Renew Energy 13:345\u2013354","journal-title":"Renew Energy"},{"key":"1469_CR29","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/S0038-092X(99)00071-7","volume":"68","author":"M Mohandes","year":"2000","unstructured":"Mohandes M, Balghonaim A, Kassas M, Rehman S, Halawani TO (2000) Use of radial basis functions for estimating monthly mean daily solar radiation. Sol Energy 68:161\u2013168","journal-title":"Sol Energy"},{"issue":"1\u20132","key":"1469_CR30","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, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1\u20132):52\u201366","journal-title":"J Hydrol"},{"key":"1469_CR31","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jhydrol.2004.06.028","volume":"301","author":"MCV Ramirez","year":"2005","unstructured":"Ramirez MCV, Velhob HFC, Ferreira NJ (2005) Artificial neural network technique for rainfall forecasting applied to the S\u00e3o Paulo region. J Hydrol 301:146\u2013162","journal-title":"J Hydrol"},{"issue":"3","key":"1469_CR32","first-page":"211","volume":"18","author":"MJ Reddy","year":"2009","unstructured":"Reddy MJ, Ghimire BNS (2009) Use of model tree and gene expression programming to predict the suspended sediment load in rivers. J Intell Syst 18(3):211\u2013228","journal-title":"J Intell Syst"},{"key":"1469_CR33","volume-title":"Applied modelling of hydrological time series","author":"JD Salas","year":"1980","unstructured":"Salas JD, Delleur JW, Yevjevich V, Lane WL (1980) Applied modelling of hydrological time series. Water Resources Publications, Littleton"},{"key":"1469_CR34","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1023\/A:1008132509589","volume":"13","author":"DA Savic","year":"1999","unstructured":"Savic DA, Walters GA, Davidson JW (1999) A genetic programming approach to rainfall-runoff modelling. Water Resour Manag 13:219\u2013231","journal-title":"Water Resour Manag"},{"key":"1469_CR35","doi-asserted-by":"crossref","first-page":"3189","DOI":"10.1002\/hyp.1096","volume":"16","author":"KP Sudheer","year":"2002","unstructured":"Sudheer KP, Gosain AK, Mohana Rangan D, Saheb SM (2002) Modelling evaporation using an artificial neural network algorithm. Hydrol Process 16:3189\u20133202","journal-title":"Hydrol Process"},{"key":"1469_CR36","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jhydrol.2009.06.014","volume":"374","author":"RSV Teegavarapu","year":"2009","unstructured":"Teegavarapu RSV, Tufail M, Ormsbee L (2009) Optimal functional forms for estimation of missing precipitation data. J Hydrol 374:106\u2013115","journal-title":"J Hydrol"},{"issue":"5","key":"1469_CR37","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1061\/(ASCE)0733-9437(2006)132:5(503)","volume":"132","author":"\u00d6 Terzi","year":"2006","unstructured":"Terzi \u00d6, Keskin ME, Taylan D (2006) Estimating evaporation using ANFIS. J Irrig Drain Eng 132(5):503\u2013507","journal-title":"J Irrig Drain Eng"},{"key":"1469_CR38","first-page":"587","volume-title":"Fuzzy and neural approaches in engineering","author":"LH Tsoukalas","year":"1997","unstructured":"Tsoukalas LH, Uhrig RE (1997) Fuzzy and neural approaches in engineering. A Wiley-Interscience Publications. John Wiley & Sons Inc., New York, p 587"},{"key":"1469_CR39","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/S0895-7177(00)00274-0","volume":"33","author":"PA Whigham","year":"2001","unstructured":"Whigham PA, Crapper PF (2001) Modeling rainfall-runoff using genetic programming. Math Comput Model 33:707\u2013721","journal-title":"Math Comput Model"},{"key":"1469_CR40","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1016\/j.engappai.2010.04.003","volume":"23","author":"CL Wu","year":"2010","unstructured":"Wu CL, Chau KW (2010) Data-driven models for monthly streamflow time series prediction. Eng Appl Artif Intell 23:1350\u20131367","journal-title":"Eng Appl Artif Intell"},{"issue":"4","key":"1469_CR41","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1080\/02508060308691721","volume":"28","author":"K Y\u00fcrekli","year":"2003","unstructured":"Y\u00fcrekli K, \u00d6zt\u00fcrk F (2003) Stochastic modeling of annual maximum and minimum streamflow of Kelkit stream. Water Int 28(4):433\u2013441","journal-title":"Water Int"},{"issue":"5","key":"1469_CR42","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/S1001-6058(09)60260-2","volume":"22","author":"FX Zhang","year":"2010","unstructured":"Zhang FX, Wai WHO, Jiang YW (2010) Jiang Prediction of sediment transportation in deep bay (Hong Kong) using genetic algorithm. J Hydrodynam 22(5):599\u2013604","journal-title":"J Hydrodynam"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/link.springer.com\/content\/pdf\/10.1007\/s00521-013-1469-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/link.springer.com\/article\/10.1007\/s00521-013-1469-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/link.springer.com\/content\/pdf\/10.1007\/s00521-013-1469-9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T20:24:26Z","timestamp":1596313466000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/link.springer.com\/10.1007\/s00521-013-1469-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,8,13]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2014,7]]}},"alternative-id":["1469"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/s00521-013-1469-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,8,13]]}}}