{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T08:03:39Z","timestamp":1767773019179,"version":"3.37.3"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"26","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s00521-024-10006-7","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T17:01:52Z","timestamp":1716829312000},"page":"16345-16364","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Prediction of precipitation using wavelet-based hybrid models considering the periodicity"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7387-0224","authenticated-orcid":false,"given":"Farshad","family":"Ahmadi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rasoul","family":"Mirabbasi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohitashw","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarita","family":"Gajbhiye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"issue":"7","key":"10006_CR1","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1002\/hyp.13391","volume":"33","author":"ZS Abdourahamane","year":"2019","unstructured":"Abdourahamane ZS, Acar R, Serkan \u015e (2019) Wavelet\u2013copula-based mutual information for rainfall forecasting applications. Hydrol Process 33(7):1127\u20131142","journal-title":"Hydrol Process"},{"issue":"3","key":"10006_CR2","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s00477-020-01910-0","volume":"35","author":"RM Adnan","year":"2021","unstructured":"Adnan RM, Petroselli A, Heddam S, Santos CAG, Kisi O (2021) Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stoch Environ Res Risk Assess 35(3):597\u2013616","journal-title":"Stoch Environ Res Risk Assess"},{"key":"10006_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11269-021-02934-z","volume":"35","author":"F Ahmadi","year":"2021","unstructured":"Ahmadi F, Mehdizadeh S, Mohammadi B (2021) Development of bio-inspired-and wavelet-based hybrid models for reconnaissance drought index modeling. Water Resour Manag 35:1\u201321","journal-title":"Water Resour Manag"},{"key":"10006_CR4","doi-asserted-by":"crossref","first-page":"106622","DOI":"10.1016\/j.agwat.2020.106622","volume":"244","author":"F Ahmadi","year":"2021","unstructured":"Ahmadi F, Mehdizadeh S, Mohammadi B, Pham QB, Doan TNC, Vo ND (2021) Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation. Agric Water Manag 244:106622","journal-title":"Agric Water Manag"},{"issue":"10","key":"10006_CR5","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1007\/s11269-014-0651-x","volume":"28","author":"SA Akrami","year":"2014","unstructured":"Akrami SA, Nourani V, Hakim SJS (2014) Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang Gates Dam. Water Resour Manag 28(10):2999\u20133018","journal-title":"Water Resour Manag"},{"issue":"2","key":"10006_CR6","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1007\/s12145-021-00603-8","volume":"14","author":"A Alizadeh","year":"2021","unstructured":"Alizadeh A, Rajabi A, Shabanlou S, Yaghoubi B, Yosefvand F (2021) Modeling long-term rainfall-runoff time series through wavelet-weighted regularization extreme learning machine. Earth Sci Inf 14(2):1047\u20131063","journal-title":"Earth Sci Inf"},{"key":"10006_CR7","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.jhydrol.2015.07.046","volume":"529","author":"A Altunkaynak","year":"2015","unstructured":"Altunkaynak A, Nigussie TA (2015) Prediction of daily rainfall by a hybrid wavelet-season-neuro technique. J Hydrol 529:287\u2013301","journal-title":"J Hydrol"},{"issue":"1","key":"10006_CR8","first-page":"1147","volume":"15","author":"SS Band","year":"2021","unstructured":"Band SS, Heggy E, Bateni SM, Karami H, Rabiee M, Samadianfard S, Mosavi A (2021) Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Eng Appl Comput Fluid Mech 15(1):1147\u20131158","journal-title":"Eng Appl Comput Fluid Mech"},{"issue":"3\u20135","key":"10006_CR9","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.pce.2009.12.002","volume":"35","author":"M Bednarik","year":"2010","unstructured":"Bednarik M, Magulov\u00e1 B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kra\u013eovany\u2013Liptovsk\u00fd Mikul\u00e1\u0161 railway case study. Phys Chem Earth Parts A\/B\/C 35(3\u20135):162\u2013171","journal-title":"Phys Chem Earth Parts A\/B\/C"},{"issue":"1","key":"10006_CR10","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"1\u20134","key":"10006_CR11","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/0022-1694(93)90172-6","volume":"144","author":"P Burlando","year":"1993","unstructured":"Burlando P, Rosso R, Cadavid LG, Salas JD (1993) Forecasting of short-term rainfall using ARMA models. J Hydrol 144(1\u20134):193\u2013211","journal-title":"J Hydrol"},{"issue":"2","key":"10006_CR12","first-page":"1","volume":"2","author":"NO Bushara","year":"2019","unstructured":"Bushara NO (2019) Weather forecasting using soft computing models: a comparative study. J Appl Sci 2(2):1\u201322","journal-title":"J Appl Sci"},{"issue":"1","key":"10006_CR13","first-page":"116","volume":"7","author":"N Bushara","year":"2015","unstructured":"Bushara N, Abraham A (2015) Novel ensemble method for long term rainfall prediction. Int J Comput Inf Syst Ind Manag Appl 7(1):116\u2013130","journal-title":"Int J Comput Inf Syst Ind Manag Appl"},{"key":"10006_CR14","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.eswa.2017.05.029","volume":"85","author":"S Cramer","year":"2017","unstructured":"Cramer S, Kampouridis M, Freitas AA, Alexandridis AK (2017) An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Syst Appl 85:169\u2013181","journal-title":"Expert Syst Appl"},{"key":"10006_CR15","unstructured":"Ebden M (2015) Gaussian processes: a quick introduction. arXiv preprint arXiv:1505.02965"},{"key":"10006_CR16","first-page":"1","volume":"26","author":"\u00d6 Ekmekcio\u011flu","year":"2020","unstructured":"Ekmekcio\u011flu \u00d6, Ba\u015fak\u0131n EE, \u00d6zger M (2020) Tree-based nonlinear ensemble technique to predict energy dissipation in stepped spillways. Eur J Environ Civ Eng 26:1\u201319","journal-title":"Eur J Environ Civ Eng"},{"issue":"7","key":"10006_CR17","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.3390\/w12071909","volume":"12","author":"J Est\u00e9vez","year":"2020","unstructured":"Est\u00e9vez J, Bellido-Jim\u00e9nez JA, Liu X, Garc\u00eda-Mar\u00edn AP (2020) Monthly precipitation forecasts using wavelet neural networks models in a semiarid environment. Water 12(7):1909","journal-title":"Water"},{"key":"10006_CR18","doi-asserted-by":"crossref","first-page":"E1396","DOI":"10.1002\/joc.6775","volume":"41","author":"M Ghamariadyan","year":"2021","unstructured":"Ghamariadyan M, Imteaz MA (2021) A wavelet artificial neural network method for medium-term rainfall prediction in Queensland (Australia) and the comparisons with conventional methods. Int J Climatol 41:E1396\u2013E1416","journal-title":"Int J Climatol"},{"issue":"6","key":"10006_CR19","doi-asserted-by":"crossref","first-page":"5375","DOI":"10.1016\/j.aej.2021.04.022","volume":"60","author":"P Ghasemi","year":"2021","unstructured":"Ghasemi P, Karbasi M, Nouri AZ, Tabrizi MS, Azamathulla HM (2021) Application of Gaussian process regression to forecast multi-step ahead SPEI drought index. Alex Eng J 60(6):5375\u20135392","journal-title":"Alex Eng J"},{"issue":"11","key":"10006_CR20","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.3390\/w11112406","volume":"11","author":"F Granata","year":"2019","unstructured":"Granata F, Di Nunno F, Gargano R, de Marinis G (2019) Equivalent discharge coefficient of side weirs in circular channel\u2014a lazy machine learning approach. Water 11(11):2406\u20132426","journal-title":"Water"},{"issue":"3","key":"10006_CR21","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s11269-017-1853-9","volume":"32","author":"M Karbasi","year":"2018","unstructured":"Karbasi M (2018) Forecasting of multi-step ahead reference evapotranspiration using wavelet-Gaussian process regression model. Water Resour Manag 32(3):1035\u20131052","journal-title":"Water Resour Manag"},{"issue":"4","key":"10006_CR22","first-page":"324","volume":"1","author":"S Kotsiantis","year":"2004","unstructured":"Kotsiantis S, Pintelas P (2004) Combining bagging and boosting. Int J Comput Intell 1(4):324\u2013333","journal-title":"Int J Comput Intell"},{"issue":"4","key":"10006_CR23","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1007\/s12145-020-00508-y","volume":"13","author":"D Kumar","year":"2020","unstructured":"Kumar D, Roshni T, Singh A, Jha MK, Samui P (2020) Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study. Earth Sci Inf 13(4):1237\u20131250","journal-title":"Earth Sci Inf"},{"key":"10006_CR24","doi-asserted-by":"crossref","first-page":"106293","DOI":"10.1016\/j.compag.2021.106293","volume":"187","author":"D Lin","year":"2021","unstructured":"Lin D, Li G, Zhu Y, Liu H, Li L, Fahad S, Jiao Q (2021) Predicting copper content in chicory leaves using hyperspectral data with continuous wavelet transforms and partial least squares. Comput Electron Agric 187:106293","journal-title":"Comput Electron Agric"},{"key":"10006_CR25","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.jhydrol.2012.03.031","volume":"442","author":"AK Lohani","year":"2012","unstructured":"Lohani AK, Kumar R, Singh RD (2012) Hydrological time series modeling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J Hydrol 442:23\u201335","journal-title":"J Hydrol"},{"key":"10006_CR26","first-page":"233","volume-title":"A wavelet tour of signal processing","author":"S Mallat","year":"1999","unstructured":"Mallat S (1999) A wavelet tour of signal processing, 2nd edn. Elsevier, Amsterdam, p 233","edition":"2"},{"issue":"6","key":"10006_CR27","doi-asserted-by":"crossref","first-page":"602","DOI":"10.3390\/atmos11060602","volume":"11","author":"L Mart\u00ednez-Acosta","year":"2020","unstructured":"Mart\u00ednez-Acosta L, Medrano-Barboza JP, L\u00f3pez-Ramos \u00c1, RemolinaL\u00f3pez JF, L\u00f3pez-Lambra\u00f1o \u00c1A (2020) SARIMA approach to generating synthetic monthly rainfall in the Sin\u00fa river watershed in Colombia. Atmosphere 11(6):602","journal-title":"Atmosphere"},{"key":"10006_CR28","doi-asserted-by":"crossref","first-page":"125017","DOI":"10.1016\/j.jhydrol.2020.125017","volume":"587","author":"S Mehdizadeh","year":"2020","unstructured":"Mehdizadeh S, Ahmadi F, Mehr AD, Safari MJS (2020) Drought modeling using classic time series and hybrid wavelet-gene expression programming models. J Hydrol 587:125017","journal-title":"J Hydrol"},{"issue":"4","key":"10006_CR29","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1002\/met.1941","volume":"27","author":"S Mehdizadeh","year":"2020","unstructured":"Mehdizadeh S, Ahmadi F, Kozekalani Sales A (2020) Modelling daily soil temperature at different depths via the classical and hybrid models. Meteorol Appl 27(4):19\u201341","journal-title":"Meteorol Appl"},{"key":"10006_CR30","first-page":"63","volume":"667","author":"AD Mehr","year":"2018","unstructured":"Mehr AD (2018) Month ahead rainfall forecasting using gene expression programming. Am J Earth Environ 667:63\u201370","journal-title":"Am J Earth Environ"},{"issue":"1","key":"10006_CR31","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s00704-020-03438-3","volume":"143","author":"AD Mehr","year":"2021","unstructured":"Mehr AD (2021) Seasonal rainfall hindcasting using ensemble multi-stage genetic programming. Theor Appl Climatol 143(1):461\u2013472","journal-title":"Theor Appl Climatol"},{"issue":"4","key":"10006_CR32","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s12145-013-0141-3","volume":"7","author":"AD Mehr","year":"2014","unstructured":"Mehr AD, Kahya E, Bagheri F, Deliktas E (2014) Successive-station monthly streamflow prediction using neuro-wavelet technique. Earth Sci Inf 7(4):217\u2013229","journal-title":"Earth Sci Inf"},{"issue":"10","key":"10006_CR33","doi-asserted-by":"crossref","first-page":"6843","DOI":"10.1007\/s00521-018-3519-9","volume":"31","author":"R Mirabbasi","year":"2019","unstructured":"Mirabbasi R, Kisi O, Sanikhani H, Meshram SG (2019) Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models. Neural Comput Appl 31(10):6843\u20136862","journal-title":"Neural Comput Appl"},{"key":"10006_CR34","unstructured":"Neal RM (1997) Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. arXiv preprint physics\/9701026"},{"key":"10006_CR35","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.jhydrol.2014.03.057","volume":"514","author":"V Nourani","year":"2014","unstructured":"Nourani V, Baghanam AH, Adamowski J, Kisi O (2014) Applications of hybrid wavelet\u2013artificial intelligence models in hydrology: a review. J Hydrol 514:358\u2013377","journal-title":"J Hydrol"},{"issue":"14","key":"10006_CR36","doi-asserted-by":"crossref","first-page":"2877","DOI":"10.1007\/s11269-009-9414-5","volume":"23","author":"V Nourani","year":"2009","unstructured":"Nourani V, Komasi M, Mano A (2009) A multivariate ANN-wavelet approach for rainfall\u2013runoff modeling. Water Resour Manag 23(14):2877\u20132894","journal-title":"Water Resour Manag"},{"issue":"3","key":"10006_CR37","first-page":"27","volume":"11","author":"A Nugroho","year":"2014","unstructured":"Nugroho A, Simanjuntak BH (2014) ARMA (autoregressive moving average) model for prediction of rainfall in regency of Semarang-Central Java-Republic of Indonesia. Int J Comput Sci Issues (IJCSI) 11(3):27","journal-title":"Int J Comput Sci Issues (IJCSI)"},{"key":"10006_CR38","doi-asserted-by":"crossref","first-page":"105851","DOI":"10.1016\/j.compag.2020.105851","volume":"179","author":"M \u00d6zger","year":"2020","unstructured":"\u00d6zger M, Ba\u015fak\u0131n EE, Ekmekcio\u011flu \u00d6, Hac\u0131s\u00fcleyman V (2020) Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction. Comput Electron Agric 179:105851","journal-title":"Comput Electron Agric"},{"issue":"3","key":"10006_CR39","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1063\/1.168573","volume":"10","author":"GD Phillies","year":"1996","unstructured":"Phillies GD (1996) Wavelets: a new alternative to Fourier transforms. Comput Phys 10(3):247\u2013252","journal-title":"Comput Phys"},{"key":"10006_CR40","first-page":"15","volume-title":"Fundamental concepts & an overview of the wavelet theory. The wavelet tutorial part I","author":"R Polikar","year":"1996","unstructured":"Polikar R (1996) Fundamental concepts & an overview of the wavelet theory. The wavelet tutorial part I. Rowan University, College of Engineering Web Servers, p 15"},{"key":"10006_CR41","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.atmosres.2017.06.014","volume":"197","author":"R Prasad","year":"2017","unstructured":"Prasad R, Deo RC, Li Y, Maraseni T (2017) Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm. Atmos Res 197:42\u201363","journal-title":"Atmos Res"},{"key":"10006_CR42","volume-title":"C4. 5: programs for machine learning","author":"JR Quinlan","year":"2014","unstructured":"Quinlan JR (2014) C4. 5: programs for machine learning. Morgan Kaurmann, San Mateo, CA"},{"issue":"2","key":"10006_CR43","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1016\/j.asej.2020.09.011","volume":"12","author":"WM Ridwan","year":"2021","unstructured":"Ridwan WM, Sapitang M, Aziz A, Kushiar KF, Ahmed AN, El-Shafie A (2021) Rainfall forecasting model using machine learning methods: case study Terengganu, Malaysia. Ain Shams Eng J 12(2):1651\u20131663","journal-title":"Ain Shams Eng J"},{"key":"10006_CR44","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Fdez I, Canosa A, Mucientes M, Bugar\u00edn A (2015) STAC: a web platform for the comparison of algorithms using statistical tests. In: Proceedings of the 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE)","DOI":"10.1109\/FUZZ-IEEE.2015.7337889"},{"issue":"2","key":"10006_CR45","doi-asserted-by":"crossref","first-page":"04018062","DOI":"10.1061\/(ASCE)HE.1943-5584.0001725","volume":"24","author":"CA Santos","year":"2019","unstructured":"Santos CA, Freire PK, Silva RMD, Akrami SA (2019) Hybrid wavelet neural network approach for daily inflow forecasting using tropical rainfall measuring mission data. J Hydrol Eng 24(2):04018062","journal-title":"J Hydrol Eng"},{"issue":"1","key":"10006_CR46","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s00704-019-03078-2","volume":"140","author":"MH Saray","year":"2020","unstructured":"Saray MH, Eslamian SS, Kl\u00f6ve B, Gohari A (2020) Regionalization of potential evapotranspiration using a modified region of influence. Theor Appl Climatol 140(1):115\u2013127","journal-title":"Theor Appl Climatol"},{"issue":"1","key":"10006_CR47","first-page":"1078","volume":"14","author":"MT Sattari","year":"2020","unstructured":"Sattari MT, Falsafian K, Irvem A, Qasem SN (2020) Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall. Eng Appl Comput Fluid Mech 14(1):1078\u20131094","journal-title":"Eng Appl Comput Fluid Mech"},{"issue":"3","key":"10006_CR48","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379\u2013423","journal-title":"Bell Syst Tech J"},{"issue":"10","key":"10006_CR49","doi-asserted-by":"crossref","first-page":"3441","DOI":"10.1007\/s11269-018-2000-y","volume":"32","author":"E Sharghi","year":"2018","unstructured":"Sharghi E, Nourani V, Najafi H, Molajou A (2018) Emotional ANN (EANN) and wavelet-ANN (WANN) approaches for Markovian and seasonal based modeling of rainfall-runoff process. Water Resour Manag 32(10):3441\u20133456","journal-title":"Water Resour Manag"},{"issue":"3","key":"10006_CR50","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2(3):160","journal-title":"SN Comput Sci"},{"key":"10006_CR51","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1016\/j.jhydrol.2018.04.042","volume":"561","author":"J Shiri","year":"2018","unstructured":"Shiri J (2018) Improving the performance of the mass transfer-based reference evapotranspiration estimation approaches through a coupled wavelet-random forest methodology. J Hydrol 561:737\u2013750","journal-title":"J Hydrol"},{"key":"10006_CR52","volume":"120C","author":"F Sohrabi Geshnigani","year":"2023","unstructured":"Sohrabi Geshnigani F, Golabi MR, Mirabbasi R, Nazeri Tahroudi M (2023) Daily solar radiation modeling using an ensemble artificial intelligence approach (case study: Illinois, USA). Eng Appl Artif Intell 120C:105839","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"10006_CR53","first-page":"195","volume":"125","author":"SM Tabatabaei","year":"2021","unstructured":"Tabatabaei SM, Tahroudi MN, Hamraz BS (2021) Comparison of the performances of GEP, ANFIS, and SVM artificial intelligence models in rainfall simulation. J Hung Meteorol Serv 125(2):195\u2013209","journal-title":"J Hung Meteorol Serv"},{"issue":"11","key":"10006_CR54","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.1007\/s11269-020-02638-w","volume":"34","author":"P Unnikrishnan","year":"2020","unstructured":"Unnikrishnan P, Jothiprakash V (2020) Hybrid SSA-ARIMA-ANN model for forecasting daily rainfall. Water Resour Manag 34(11):3609\u20133623","journal-title":"Water Resour Manag"},{"issue":"4","key":"10006_CR55","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3390\/agriculture6040053","volume":"6","author":"M Valipour","year":"2016","unstructured":"Valipour M (2016) How much meteorological information is necessary to achieve reliable accuracy for rainfall estimations? Agriculture 6(4):53","journal-title":"Agriculture"},{"issue":"15","key":"10006_CR56","first-page":"1987","volume":"13","author":"H Wang","year":"2021","unstructured":"Wang H, Wang W, Du Y, Xu D (2021) Examining the applicability of wavelet packet decomposition on different forecasting models in annual rainfall prediction. Water 13(15):1987\u20131997","journal-title":"Water"},{"issue":"1","key":"10006_CR57","first-page":"67","volume":"1","author":"W Wang","year":"2003","unstructured":"Wang W, Ding J (2003) Wavelet network model and its application to the prediction of hydrology. Nat Sci 1(1):67\u201371","journal-title":"Nat Sci"},{"issue":"13","key":"10006_CR58","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1002\/joc.2419","volume":"32","author":"CJ Willmott","year":"2012","unstructured":"Willmott CJ, Robeson SM, Matsuura K (2012) A refined index of model performance. Int J Climatol 32(13):2088\u20132094","journal-title":"Int J Climatol"},{"key":"10006_CR59","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.asoc.2018.09.018","volume":"73","author":"Y Xiang","year":"2018","unstructured":"Xiang Y, Gou L, He L, Xia S, Wang W (2018) A SVR\u2013ANN combined model based on ensemble EMD for rainfall prediction. Appl Soft Comput 73:874\u2013883","journal-title":"Appl Soft Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10006-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10006-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10006-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T18:13:19Z","timestamp":1724436799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10006-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,27]]},"references-count":59,"journal-issue":{"issue":"26","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["10006"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10006-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,5,27]]},"assertion":[{"value":"1 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}