{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T02:28:49Z","timestamp":1773541729669,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2015,9,10]],"date-time":"2015-09-10T00:00:00Z","timestamp":1441843200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100005417","name":"Universiti Teknologi Malaysia (MY)","doi-asserted-by":"publisher","award":["Post-doctoral grant"],"award-info":[{"award-number":["Post-doctoral grant"]}],"id":[{"id":"10.13039\/501100005417","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2016,11]]},"DOI":"10.1007\/s00521-015-2024-7","type":"journal-article","created":{"date-parts":[[2015,9,10]],"date-time":"2015-09-10T14:32:02Z","timestamp":1441895522000},"page":"2551-2565","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A new rainfall forecasting model using the CAPSO algorithm and an artificial neural network"],"prefix":"10.1007","volume":"27","author":[{"given":"Zahra","family":"Beheshti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Morteza","family":"Firouzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siti Mariyam","family":"Shamsuddin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masoumeh","family":"Zibarzani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zulkifli","family":"Yusop","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,9,10]]},"reference":[{"key":"2024_CR1","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1175\/1520-0434(1995)010<0498:EOYOQP>2.0.CO;2","volume":"10","author":"D Olson","year":"1995","unstructured":"Olson D, Junker N, Korty B (1995) Evaluation of 33\u00a0years of quantitative precipitation forecasting at the NMC. Weather Forecast 10:498\u2013511","journal-title":"Weather Forecast"},{"issue":"1","key":"2024_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1061\/(ASCE)1084-0699(2004)9:1(1)","volume":"9","author":"J Olsson","year":"2004","unstructured":"Olsson J et al (2004) Neural networks for rainfall forecasting by atmospheric downscaling. J Hydrol Eng 9(1):1\u201312","journal-title":"J Hydrol Eng"},{"key":"2024_CR3","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1029\/WR020i011p01597","volume":"20","author":"K Georgakakos","year":"1984","unstructured":"Georgakakos K, Bras R (1984) A hydrologically useful station precipitation model: 2. Case studies. Water Resour Res 20:1597\u20131610","journal-title":"Water Resour Res"},{"key":"2024_CR4","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1029\/WR020i011p01585","volume":"20","author":"K Georgakakos","year":"1984","unstructured":"Georgakakos K, Bras R (1984) A hydrologically useful station precipitation model 1. Formulation. Water Resour Res 20:1585\u20131596","journal-title":"Water Resour Res"},{"key":"2024_CR5","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, Ki\u015fi \u00d6 (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199\u2013212","journal-title":"J Hydrol"},{"key":"2024_CR6","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1016\/j.engappai.2011.11.003","volume":"25","author":"O Kisi","year":"2012","unstructured":"Kisi O, Cimen M (2012) Engineering applications of artificial intelligence precipitation forecasting by using wavelet-support vector machine conjunction model. Eng Appl Artif Intell 25:783\u2013792","journal-title":"Eng Appl Artif Intell"},{"key":"2024_CR7","unstructured":"Halff AH, Halff HM, Azmoodeh M (1993) Predicting runoff from rainfall using neural networks. Eng Hydrol. ASCE, pp 760\u2013765"},{"key":"2024_CR8","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/978-94-017-3083-9_16","volume-title":"Stochastic and statistical methods in hydrology and environmental engineering","author":"M-L Zhu","year":"1994","unstructured":"Zhu M-L, Fujita M, Hashimoto N (1994) Application of neural networks to runoff prediction. In: Hipel KW et al (eds) Stochastic and statistical methods in hydrology and environmental engineering. Springer, Dordrecht, pp 205\u2013216"},{"key":"2024_CR9","doi-asserted-by":"crossref","first-page":"3908","DOI":"10.1175\/MWR-D-13-00012.1","volume":"141","author":"X Fang","year":"2013","unstructured":"Fang X, Kuo Y-H (2013) Improving ensemble-based quantitative precipitation forecasts for topography-enhanced typhoon heavy rainfall over Taiwan with a modified probability-matching technique. Mon Weather Rev 141:3908\u20133932","journal-title":"Mon Weather Rev"},{"key":"2024_CR10","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1061\/(ASCE)0887-3801(1994)8:2(149)","volume":"8","author":"I Flood","year":"1994","unstructured":"Flood I, Kartam N (1994) Neural networks in civil engineering. II: systems and application. J Comput Civ Eng 8:149\u2013162","journal-title":"J Comput Civ Eng"},{"key":"2024_CR11","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/S0895-7177(00)00272-7","volume":"33","author":"K Luk","year":"2001","unstructured":"Luk K, Ball J, Sharma A (2001) An application of artificial neural networks for rainfall forecasting. Math Comput Model 33:683\u2013693","journal-title":"Math Comput Model"},{"key":"2024_CR12","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jhydrol.2004.06.028","volume":"301","author":"MC Valverde Ram\u00edrez","year":"2005","unstructured":"Valverde Ram\u00edrez MC, de Campos Velho HF, 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"},{"key":"2024_CR13","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00704-011-0575-9","volume":"109","author":"S Azadi","year":"2012","unstructured":"Azadi S, Sepaskhah A (2012) Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks. Theor Appl Climatol 109:175\u2013189","journal-title":"Theor Appl Climatol"},{"key":"2024_CR14","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jhydrol.2013.08.035","volume":"503","author":"F Mekanika","year":"2013","unstructured":"Mekanika F et al (2013) Multiple regression and artificial neural network for long-term rainfall forecasting using large scale climate modes. J Hydrol 503:11\u201321","journal-title":"J Hydrol"},{"key":"2024_CR15","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.atmosres.2011.07.020","volume":"119","author":"PT Nastos","year":"2013","unstructured":"Nastos PT et al (2013) Rain intensity forecast using artificial neural networks in Athens, Greece. Atmos Res 119:153\u2013160","journal-title":"Atmos Res"},{"key":"2024_CR16","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.neucom.2012.10.043","volume":"148","author":"J Wu","year":"2015","unstructured":"Wu J, Long J, Liu M (2015) Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 148:136\u2013142","journal-title":"Neurocomputing"},{"key":"2024_CR17","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1061\/(ASCE)1084-0699(1999)4:3(232)","volume":"4","author":"A Tokar","year":"1999","unstructured":"Tokar A, Johnson P (1999) Rainfall\u2013runoff modeling using artificial neural networks. J Hydrol Eng 4:232\u2013239","journal-title":"J Hydrol Eng"},{"key":"2024_CR18","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.jhydrol.2003.08.011","volume":"285","author":"M Rajurkar","year":"2004","unstructured":"Rajurkar M, Kothyari U, Chaube U (2004) Modeling of the daily rainfall\u2013runoff relationship with artificial neural network. J Hydrol 285:96\u2013113","journal-title":"J Hydrol"},{"key":"2024_CR19","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1007\/s11269-009-9573-4","volume":"24","author":"M Rezaeian Zadeh","year":"2010","unstructured":"Rezaeian Zadeh M et al (2010) Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manage 24:2673\u20132688","journal-title":"Water Resour Manage"},{"key":"2024_CR20","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1007\/s12517-011-0517-y","volume":"6","author":"M Rezaeian-Zadeh","year":"2012","unstructured":"Rezaeian-Zadeh M, Tabari H, Abghari H (2012) Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions. Arab J Geosci 6:2529\u20132537","journal-title":"Arab J Geosci"},{"key":"2024_CR21","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s00704-012-0592-3","volume":"109","author":"M Rezaeian-Zadeh","year":"2012","unstructured":"Rezaeian-Zadeh M, Tabari H (2012) MLP-based drought forecasting in different climatic regions. Theor Appl Climatol 109:407\u2013414","journal-title":"Theor Appl Climatol"},{"key":"2024_CR22","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s00521-013-1443-6","volume":"25","author":"M Rezaeianzadeh","year":"2014","unstructured":"Rezaeianzadeh M et al (2014) Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput Appl 25:25\u201337","journal-title":"Neural Comput Appl"},{"key":"2024_CR23","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jhydrol.2004.06.021","volume":"301","author":"G Bowden","year":"2005","unstructured":"Bowden G, Dandy G, Maier H (2005) Input determination for neural network models in water resources applications. Part 1\u2014background and methodology. J Hydrol 301:75\u201392","journal-title":"J Hydrol"},{"key":"2024_CR24","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1016\/j.ecolmodel.2009.01.004","volume":"220","author":"K Singh","year":"2009","unstructured":"Singh K et al (2009) Artificial neural network modeling of the river water quality\u2014a case study. Ecol Model 220:888\u2013895","journal-title":"Ecol Model"},{"key":"2024_CR25","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1061\/(ASCE)0733-9437(2002)128:4(224)","volume":"128","author":"M Kumar","year":"2002","unstructured":"Kumar M, Raghuwanshi N (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128:224\u2013233","journal-title":"J Irrig Drain Eng"},{"key":"2024_CR26","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1007\/s00521-012-1087-y","volume":"23","author":"M-B Aghajanloo","year":"2012","unstructured":"Aghajanloo M-B, Sabziparvar A-A, Hosseinzadeh Talaee P (2012) Artificial neural network\u2013genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran. Neural Comput Appl 23:1387\u20131393","journal-title":"Neural Comput Appl"},{"key":"2024_CR27","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s00521-012-0904-7","volume":"23","author":"H Tabari","year":"2013","unstructured":"Tabari H, Hosseinzadeh Talaee P (2013) Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Comput Appl 23:341\u2013348","journal-title":"Neural Comput Appl"},{"key":"2024_CR28","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.jhydrol.2004.12.001","volume":"309","author":"IN Daliakopoulos","year":"2005","unstructured":"Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229\u2013240","journal-title":"J Hydrol"},{"key":"2024_CR29","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.jhydrol.2004.10.005","volume":"307","author":"S Lallahem","year":"2005","unstructured":"Lallahem S et al (2005) On the use of neural networks to evaluate groundwater levels in fractured media. J Hydrol 307:92\u2013111","journal-title":"J Hydrol"},{"key":"2024_CR30","unstructured":"ASCE (2000) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrol Eng 124\u2013137"},{"key":"2024_CR31","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1061\/(ASCE)1084-0699(2000)5:2(115)","volume":"5","author":"ASCE","year":"2000","unstructured":"ASCE (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5:115\u2013123","journal-title":"J Hydrol Eng"},{"key":"2024_CR32","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S1364-8152(99)00007-9","volume":"15","author":"H Maier","year":"2000","unstructured":"Maier H, Dandy G (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101\u2013124","journal-title":"Environ Model Softw"},{"key":"2024_CR33","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1061\/(ASCE)0887-3801(1994)8:2(131)","volume":"8","author":"I Flood","year":"1994","unstructured":"Flood I, Kartam N (1994) Neural networks in civil engineering. I: principles and understanding. J Comput Civ Eng 8:131\u2013148","journal-title":"J Comput Civ Eng"},{"key":"2024_CR34","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1623\/hysj.54.2.234","volume":"54","author":"T Partal","year":"2009","unstructured":"Partal T, Cigizoglu H (2009) Prediction of daily precipitation using wavelet\u2014neural networks. Hydrol Sci J 54:234\u2013246","journal-title":"Hydrol Sci J"},{"key":"2024_CR35","doi-asserted-by":"crossref","first-page":"3697","DOI":"10.1007\/s11269-013-0374-4","volume":"27","author":"R Ramana","year":"2013","unstructured":"Ramana R et al (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manage 27:3697\u20133711","journal-title":"Water Resour Manage"},{"key":"2024_CR36","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1016\/j.eswa.2007.08.033","volume":"35","author":"M Nasseri","year":"2008","unstructured":"Nasseri M, Asghari K, Abedini M (2008) Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Syst Appl 35:1415\u20131421","journal-title":"Expert Syst Appl"},{"issue":"4","key":"2024_CR37","doi-asserted-by":"crossref","first-page":"458","DOI":"10.2166\/hydro.2010.032","volume":"12","author":"KW Chau","year":"2010","unstructured":"Chau KW, Wu CL (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinform 12(4):458\u2013473","journal-title":"J Hydroinform"},{"key":"2024_CR38","doi-asserted-by":"crossref","unstructured":"Zhao H, Jin L, Huang X (2010) A prediction of the monthly precipitation model based on PSO-ANN and its applications. In: Third international joint conference on computational science and optimization, IEEE","DOI":"10.1109\/CSO.2010.20"},{"key":"2024_CR39","unstructured":"Wu J, Wang L, Zhu B (2006) The meteorological prediction model study of neural ensemble based on PSO algorithms. In: The 6th world congress on intelligent control and automation. Dalian, China"},{"key":"2024_CR40","doi-asserted-by":"crossref","unstructured":"Wu J, Chen E (2009) A novel nonparametric regression ensemble for rainfall forecasting using particle swarm optimization technique coupled with artificial neural network. In: Yu W, He H, Zhang N (eds) Proceedings of the 6th international symposium on neural networks, ISNN 2009 Wuhan, 26\u201329 May 2009, Part III. Lecture notes in computer science, vol 5553. Springer, Berlin, pp 49\u201358","DOI":"10.1007\/978-3-642-01513-7_6"},{"key":"2024_CR41","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.amc.2007.10.046","volume":"200","author":"W-C Hong","year":"2008","unstructured":"Hong W-C (2008) Rainfall forecasting by technological machine learning models. Appl Math Comput 200:41\u201357","journal-title":"Appl Math Comput"},{"issue":"2","key":"2024_CR42","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1142\/S1469026810002793","volume":"9","author":"J Wu","year":"2010","unstructured":"Wu J, Liu M, Jin L (2010) A hybrid support vector regression approach for rainfall forecasting using particle swarm optimization and projection pursuit technology. Int J Comput Intell Appl 9(2):87\u2013104","journal-title":"Int J Comput Intell Appl"},{"key":"2024_CR43","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/5236.001.0001","volume-title":"Parallel distributed processing: explorations in the microstructure of cognition: foundations","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, McClelland J (1986) Parallel distributed processing: explorations in the microstructure of cognition: foundations. MIT Press, Cambridge"},{"issue":"3","key":"2024_CR44","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/72.97911","volume":"2","author":"RP Brent","year":"1991","unstructured":"Brent RP (1991) Fast training algorithms for multilayer neural nets. Neural Netw IEEE Trans 2(3):346\u2013354","journal-title":"Neural Netw IEEE Trans"},{"issue":"1","key":"2024_CR45","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/34.107014","volume":"14","author":"M Gori","year":"1992","unstructured":"Gori M, Tesi A (1992) On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Mach Intell 14(1):76\u201386","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2024_CR46","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.ins.2013.08.015","volume":"258","author":"Z Beheshti","year":"2014","unstructured":"Beheshti Z, Shamsuddin SM (2014) CAPSO: centripetal accelerated particle swarm optimization. Inf Sci 258:54\u201379","journal-title":"Inf Sci"},{"issue":"13","key":"2024_CR47","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232\u20132248","journal-title":"Inf Sci"},{"key":"2024_CR48","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511807800","volume-title":"Gravity from the ground up: an introductory guide to gravity and general relativity","author":"B Schutz","year":"2003","unstructured":"Schutz B (2003) Gravity from the ground up: an introductory guide to gravity and general relativity. Cambridge University Press, Cambridge"},{"key":"2024_CR49","volume-title":"Fundamentals of physics","author":"D Halliday","year":"1993","unstructured":"Halliday D, Resnick R (1993) Fundamentals of physics. Wiley, New York"},{"key":"2024_CR50","doi-asserted-by":"crossref","unstructured":"Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In Evolutionary computation, 2007. CEC 2007. IEEE congress on, IEEE","DOI":"10.1109\/CEC.2007.4425083"},{"issue":"9","key":"2024_CR51","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1109\/5.784219","volume":"87","author":"X Yao","year":"1999","unstructured":"Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423\u20131447","journal-title":"Proc IEEE"},{"key":"2024_CR52","doi-asserted-by":"crossref","unstructured":"Beheshti Z (2013) Centripetal accelerated particle swarm optimization and its applications in machine learning. Universiti Teknologi Malaysia","DOI":"10.1016\/j.ins.2013.08.015"},{"issue":"4","key":"2024_CR53","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/BF02279931","volume":"2","author":"F Lisi","year":"1995","unstructured":"Lisi F, Nicolis O, Sandri M (1995) Combining singular-spectrum analysis and neural networks for time series forecasting. Neural Process Lett 2(4):6\u201310","journal-title":"Neural Process Lett"},{"key":"2024_CR54","doi-asserted-by":"crossref","first-page":"141","DOI":"10.2166\/hydro.2001.0014","volume":"3","author":"C Sivapragasam","year":"2001","unstructured":"Sivapragasam C, Liong S, Pasha M (2001) Rainfall and runoff forecasting with SSA-SVM approach. J Hydroinform 3:141\u2013152","journal-title":"J Hydroinform"},{"issue":"3","key":"2024_CR55","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/S0893-6080(03)00022-4","volume":"16","author":"D Baratta","year":"2003","unstructured":"Baratta D et al (2003) Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting. Neural Netw 16(3):375\u2013387","journal-title":"Neural Netw"},{"key":"2024_CR56","doi-asserted-by":"crossref","DOI":"10.1201\/9781420035841","volume-title":"Analysis of time series structure: SSA and related techniques","author":"N Golyandina","year":"2001","unstructured":"Golyandina N, Nekrutkin V, Zhigljavsky AA (2001) Analysis of time series structure: SSA and related techniques. CRC Press, Boca Raton"},{"issue":"3","key":"2024_CR57","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","volume":"10","author":"J Nash","year":"1970","unstructured":"Nash J, Sutcliffe J (1970) River flow forecasting through conceptual models part I\u2014A discussion of principles. J Hydrol 10(3):282\u2013290","journal-title":"J Hydrol"},{"issue":"2","key":"2024_CR58","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/02723646.1981.10642213","volume":"2","author":"CJ Willmott","year":"1981","unstructured":"Willmott CJ (1981) On the validation of models. Phys Geogr 2(2):184\u2013194","journal-title":"Phys Geogr"},{"issue":"2","key":"2024_CR59","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1061\/(ASCE)0887-3801(1994)8:2(201)","volume":"8","author":"N Karunanithi","year":"1994","unstructured":"Karunanithi N et al (1994) Neural networks for river flow prediction. J Comput Civ Eng 8(2):201\u2013220","journal-title":"J Comput Civ Eng"},{"issue":"8","key":"2024_CR60","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861\u2013874","journal-title":"Pattern Recogn Lett"},{"key":"2024_CR61","unstructured":"Beheshti Z et al (2013) Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis. Soft Comput 1\u201318"},{"key":"2024_CR62","first-page":"1","volume":"5","author":"Z Beheshti","year":"2013","unstructured":"Beheshti Z, Shamsudding SM (2013) A review of population-based meta-heuristic algorithms. Int J Adv Soft Comput Appl 5:1\u201335","journal-title":"Int J Adv Soft Comput Appl"},{"key":"2024_CR63","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jhydrol.2010.05.040","volume":"389","author":"C Wu","year":"2010","unstructured":"Wu C, Chau K, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389:146\u2013167","journal-title":"J Hydrol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2024-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-015-2024-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2024-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-2024-7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T16:24:55Z","timestamp":1653150295000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-015-2024-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,9,10]]},"references-count":63,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2016,11]]}},"alternative-id":["2024"],"URL":"https:\/\/doi.org\/10.1007\/s00521-015-2024-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,9,10]]}}}