{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T12:45:20Z","timestamp":1783687520323,"version":"3.55.0"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"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":["Soft Comput"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s00500-021-06009-4","type":"journal-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T08:02:42Z","timestamp":1626076962000},"page":"10723-10748","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0887-1217","authenticated-orcid":false,"given":"Akram","family":"Seifi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Ehteram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Nayebloei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Soroush","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0454-2811","authenticated-orcid":false,"given":"Bahram","family":"Gharabaghi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Torabi Haghighi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"6009_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-030-10674-4","volume-title":"Feature selection and enhanced krill herd algorithm for text document clustering","author":"LMQ Abualigah","year":"2019","unstructured":"Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin, pp 1\u2013165"},{"key":"6009_CR2","doi-asserted-by":"crossref","unstructured":"Abualigah L and Diabat A (2021) Advances in sine cosine algorithm: a comprehensive survey. Art Intell Rev, 1\u201342.","DOI":"10.1007\/s10462-020-09909-3"},{"key":"6009_CR3","doi-asserted-by":"crossref","first-page":"107250","DOI":"10.1016\/j.cie.2021.107250","volume":"157","author":"L Abualigah","year":"2021","unstructured":"Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Indus Eng 157:107250","journal-title":"Comput Indus Eng"},{"key":"6009_CR4","doi-asserted-by":"crossref","first-page":"113609","DOI":"10.1016\/j.cma.2020.113609","volume":"376","author":"L Abualigah","year":"2021","unstructured":"Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"5","key":"6009_CR5","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s12517-016-2388-8","volume":"9","author":"HZ Abyaneh","year":"2016","unstructured":"Abyaneh HZ, Varkeshi MB, Golmohammadi G, Mohammadi K (2016) Soil temperature estimation using an artificial neural network and co-active neuro-fuzzy inference system in two different climates. Arab J Geosci 9(5):377","journal-title":"Arab J Geosci"},{"key":"6009_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09915-5","author":"M Alizamir","year":"2020","unstructured":"Alizamir M, Kim S, Zounemat-Kermani M, Heddam S, Shahrabadi AH, Gharabaghi B (2020) Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model. Artif Intell Rev. https:\/\/doi.org\/10.1007\/s10462-020-09915-5","journal-title":"Artif Intell Rev"},{"key":"6009_CR7","doi-asserted-by":"crossref","unstructured":"Alor A, Mota D, Olmos-S\u00e1nchez K, Rodas-Osollo J (2019) An order-picking model associated with hospital components and solved by a firefly algorithm. In handbook of research on metaheuristics for order picking optimization in warehouses to smart cities (pp. 173\u2013188). IGI Global.","DOI":"10.4018\/978-1-5225-8131-4.ch009"},{"issue":"4","key":"6009_CR8","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1002\/met.1661","volume":"24","author":"A Araghi","year":"2017","unstructured":"Araghi A, Mousavi-Baygi M, Adamowski J, Martinez C, van der Ploeg M (2017) Forecasting soil temperature based on surface air temperature using a wavelet artificial neural network. Meteorol Appl 24(4):603\u2013611","journal-title":"Meteorol Appl"},{"key":"6009_CR9","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1007\/s10040-020-02115-z","volume":"28","author":"S Azadi","year":"2020","unstructured":"Azadi S, Amiri H, Ataei P, Javadpour S (2020) Optimal design of groundwater monitoring networks using gamma test theory. Hydrogeol J 28:1389\u20131402","journal-title":"Hydrogeol J"},{"key":"6009_CR10","doi-asserted-by":"crossref","first-page":"109483","DOI":"10.1016\/j.rser.2019.109483","volume":"117","author":"AH Bademlioglu","year":"2020","unstructured":"Bademlioglu AH, Canbolat AS, Kaynakli O (2020) Multi-objective optimization of parameters affecting organic rankine cycle performance characteristics with Taguchi-Grey relational analysis. Ren Sustain Energy Rev 117:109483","journal-title":"Ren Sustain Energy Rev"},{"issue":"2","key":"6009_CR11","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1007\/s12665-017-6395-1","volume":"76","author":"J Behmanesh","year":"2017","unstructured":"Behmanesh J, Mehdizadeh S (2017) Estimation of soil temperature using gene expression programming and artificial neural networks in a semiarid region. Environ Earth Sci 76(2):76","journal-title":"Environ Earth Sci"},{"issue":"3\u20134","key":"6009_CR12","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1007\/s00704-018-2436-2","volume":"135","author":"H Bonakdari","year":"2019","unstructured":"Bonakdari H, Moeeni H, Ebtehaj I, Zeynoddin M, Mahoammadian A, Gharabaghi B (2019) New insights into soil temperature time series modeling: linear or nonlinear? Theoret Appl Climatol 135(3\u20134):1157\u20131177. https:\/\/doi.org\/10.1007\/s00704-018-2436-2","journal-title":"Theoret Appl Climatol"},{"key":"6009_CR13","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.jclepro.2019.05.020","volume":"229","author":"AS Canbolat","year":"2019","unstructured":"Canbolat AS, Bademlioglu AH, Arslanoglu N, Kaynakli O (2019) Performance optimization of absorption refrigeration systems using Taguchi, ANOVA and Grey Relational Analysis methods. J Clean Prod 229:874\u2013885","journal-title":"J Clean Prod"},{"issue":"1\u20132","key":"6009_CR14","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s00704-016-1914-7","volume":"130","author":"H Citakoglu","year":"2017","unstructured":"Citakoglu H (2017) Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey. Theoret Appl Climatol 130(1\u20132):545\u2013556","journal-title":"Theoret Appl Climatol"},{"issue":"3","key":"6009_CR15","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"key":"6009_CR16","doi-asserted-by":"crossref","first-page":"101216","DOI":"10.1016\/j.ecoinf.2021.101216","volume":"61","author":"P Damos","year":"2021","unstructured":"Damos P, Caballero P (2021) Detecting seasonal transient correlations between populations of the West Nile Virus vector Culex sp and temperatures with wavelet coherence analysis. Ecological Informatics 61:101216","journal-title":"Ecological Informatics"},{"issue":"3\u20134","key":"6009_CR17","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1007\/s00704-018-2370-3","volume":"135","author":"M Delbari","year":"2019","unstructured":"Delbari M, Sharifazari S, Mohammadi E (2019) Modeling daily soil temperature over diverse climate conditions in Iran\u2014a comparison of multiple linear regression and support vector regression techniques. Theoret Appl Climatol 135(3\u20134):991\u20131001","journal-title":"Theoret Appl Climatol"},{"key":"6009_CR18","doi-asserted-by":"publisher","unstructured":"Ebrahimi A, Rahimi D, Joghataei M, Movahedi S (2021) Correlation wavelet analysis for linkage between winter precipitation and three oceanic sources in Iran. Environ Process 1\u201319. https:\/\/doi.org\/10.1007\/s40710-021-00524-0","DOI":"10.1007\/s40710-021-00524-0"},{"key":"6009_CR19","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.geoderma.2018.11.044","volume":"338","author":"Y Feng","year":"2019","unstructured":"Feng Y, Cui N, Hao W, Gao L, Gong D (2019) Estimation of soil temperature from meteorological data using different machine learning models. Geoderma 338:67\u201377","journal-title":"Geoderma"},{"issue":"8\u20139","key":"6009_CR20","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1016\/j.agrformet.2008.03.006","volume":"148","author":"AC Furon","year":"2008","unstructured":"Furon AC, Wagner-Riddle C, Smith CR, Warland JS (2008) Wavelet analysis of wintertime and spring thaw CO2 and N2O fluxes from agricultural fields. Agric For Meteorol 148(8\u20139):1305\u20131317","journal-title":"Agric For Meteorol"},{"key":"6009_CR21","doi-asserted-by":"crossref","unstructured":"Han D, Yan W, Nia AM (2010) Uncertainty with the Gamma Test for model input data selection. In The 2010 International Joint Conference on Neural Networks (IJCNN) (pp. 1\u20135). IEEE.","DOI":"10.1109\/IJCNN.2010.5596827"},{"issue":"3","key":"6009_CR22","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s40808-018-0565-3","volume":"5","author":"S Heddam","year":"2019","unstructured":"Heddam S (2019) Development of air\u2013soil temperature model using computational intelligence paradigms: artificial neural network versus multiple linear regression. Model Earth Syst Environ 5(3):747\u2013751","journal-title":"Model Earth Syst Environ"},{"issue":"1","key":"6009_CR23","first-page":"506","volume":"12","author":"SMR Kazemi","year":"2018","unstructured":"Kazemi SMR, Minaei Bidgoli B, Shamshirband S, Karimi SM, Ghorbani MA, Chau KW, Kazem Pour R (2018) Novel genetic-based negative correlation learning for estimating soil temperature. Eng Appl Comput Fluid Mech 12(1):506\u2013516","journal-title":"Eng Appl Comput Fluid Mech"},{"issue":"1\u20132","key":"6009_CR24","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s00704-014-1232-x","volume":"121","author":"O Kisi","year":"2015","unstructured":"Kisi O, Tombul M, Kermani MZ (2015) Modeling soil temperatures at different depths by using three different neural computing techniques. Theoret Appl Climatol 121(1\u20132):377\u2013387","journal-title":"Theoret Appl Climatol"},{"issue":"3\u20134","key":"6009_CR25","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/s00704-016-1810-1","volume":"129","author":"O Kisi","year":"2017","unstructured":"Kisi O, Sanikhani H, Cobaner M (2017) Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques. Theoret Appl Climatol 129(3\u20134):833\u2013848","journal-title":"Theoret Appl Climatol"},{"key":"6009_CR26","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.jhydrol.2019.05.023","volume":"575","author":"E Lee","year":"2019","unstructured":"Lee E, Kim S (2019) Wavelet analysis of soil moisture measurements for hillslope hydrological processes. J Hydrol 575:82\u201393","journal-title":"J Hydrol"},{"issue":"1","key":"6009_CR27","first-page":"5","volume":"45","author":"M Lehnert","year":"2014","unstructured":"Lehnert M (2014) Factors affecting soil temperature as limits of spatial interpretation and simulation of soil temperature. Acta Universitatis Palackianae Olomucensis-Geographica 45(1):5\u201321","journal-title":"Acta Universitatis Palackianae Olomucensis-Geographica"},{"key":"6009_CR28","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.agee.2019.04.016","volume":"279","author":"X Li","year":"2019","unstructured":"Li X, Xu X, Liu W, He L, Xu C, Zhang R, Wang K (2019) Revealing the scale-specific influence of meteorological controls on soil water content in a karst depression using wavelet coherency. Agr Ecosyst Environ 279:89\u201399","journal-title":"Agr Ecosyst Environ"},{"key":"6009_CR29","doi-asserted-by":"crossref","first-page":"59427","DOI":"10.1109\/ACCESS.2020.2982996","volume":"8","author":"Q Li","year":"2020","unstructured":"Li Q, Hao H, Zhao Y, Geng Q, Liu G, Zhang Y, Yu F (2020) GANs-LSTM model for soil temperature estimation from meteorological: a new approach. IEEE Access 8:59427\u201359443","journal-title":"IEEE Access"},{"issue":"8","key":"6009_CR30","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s12665-017-6607-8","volume":"76","author":"S Mehdizadeh","year":"2017","unstructured":"Mehdizadeh S, Behmanesh J, Khalili K (2017) Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data. Environ Earth Sci 76(8):325","journal-title":"Environ Earth Sci"},{"key":"6009_CR31","doi-asserted-by":"crossref","first-page":"108127","DOI":"10.1016\/j.measurement.2020.108127","volume":"165","author":"S Mehdizadeh","year":"2020","unstructured":"Mehdizadeh S, Mohammadi B, Pham QB, Khoi DN, Linh NTT (2020) Implementing novel hybrid models to improve indirect measurement of the daily soil temperature: Elman neural network coupled with gravitational search algorithm and ant colony optimization. Measurement 165:108127","journal-title":"Measurement"},{"key":"6009_CR32","doi-asserted-by":"crossref","first-page":"104513","DOI":"10.1016\/j.still.2019.104513","volume":"197","author":"S Mehdizadeh","year":"2020","unstructured":"Mehdizadeh S, Fathian F, Safari MJS, Khosravi A (2020) Developing novel hybrid models for estimation of daily soil temperature at various depths. Soil Tillage Res 197:104513","journal-title":"Soil Tillage Res"},{"key":"6009_CR33","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","volume":"114","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163\u2013191","journal-title":"Adv Eng Softw"},{"key":"6009_CR34","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.geoderma.2019.06.028","volume":"353","author":"R Moazenzadeh","year":"2019","unstructured":"Moazenzadeh R, Mohammadi B (2019) Assessment of bio-inspired metaheuristic optimisation algorithms for estimating soil temperature. Geoderma 353:152\u2013171","journal-title":"Geoderma"},{"issue":"3","key":"6009_CR35","doi-asserted-by":"crossref","first-page":"885","DOI":"10.13031\/2013.23153","volume":"50","author":"DN Moriasi","year":"2007","unstructured":"Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885\u2013900","journal-title":"Trans ASABE"},{"issue":"6","key":"6009_CR36","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.13031\/trans.58.10715","volume":"58","author":"DN Moriasi","year":"2015","unstructured":"Moriasi DN, Gitau MW, Pai N, Daggupati P (2015) Hydrologic and water quality models: performance measures and evaluation criteria. Trans ASABE 58(6):1763\u20131785","journal-title":"Trans ASABE"},{"key":"6009_CR37","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.compag.2016.03.025","volume":"124","author":"B Nahvi","year":"2016","unstructured":"Nahvi B, Habibi J, Mohammadi K, Shamshirband S, Al Razgan OS (2016) Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature. Comput Electron Agric 124:150\u2013160","journal-title":"Comput Electron Agric"},{"issue":"7","key":"6009_CR38","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1071\/SR18352","volume":"57","author":"M Najafi-Ghiri","year":"2019","unstructured":"Najafi-Ghiri M, Mokarram M, Owliaie HR (2019) Prediction of soil clay minerals from some soil properties with use of feature selection algorithm and ANFIS methods. Soil Research 57(7):788\u2013796","journal-title":"Soil Research"},{"issue":"3","key":"6009_CR39","doi-asserted-by":"crossref","first-page":"713","DOI":"10.3390\/w12030713","volume":"12","author":"A Nanda","year":"2020","unstructured":"Nanda A, Sen S, Sharma AN, Sudheer KP (2020) Soil Temperature dynamics at hillslope scale\u2014field observation and machine learning-based approach. Water 12(3):713","journal-title":"Water"},{"issue":"1","key":"6009_CR40","doi-asserted-by":"crossref","first-page":"35","DOI":"10.5194\/soil-6-35-2020","volume":"6","author":"J Padarian","year":"2020","unstructured":"Padarian J, Minasny B, McBratney AB (2020) Machine learning and soil sciences: a review aided by machine learning tools. Soil 6(1):35\u201352","journal-title":"Soil"},{"key":"6009_CR41","doi-asserted-by":"publisher","unstructured":"Panahi F, Ehteram M, Emami M (2021) Suspended sediment load prediction based on soft computing models and Black Widow Optimization Algorithm using an enhanced gamma test. Environ Sci Pollut Res 1\u201321. https:\/\/doi.org\/10.1007\/s11356-021-14065-4","DOI":"10.1007\/s11356-021-14065-4"},{"key":"6009_CR42","doi-asserted-by":"crossref","first-page":"51884","DOI":"10.1109\/ACCESS.2020.2979822","volume":"8","author":"L Penghui","year":"2020","unstructured":"Penghui L, Ewees AA, Beyaztas BH, Qi C, Salih SQ, Al-Ansari N, Singh VP (2020) Metaheuristic optimization algorithms hybridized with artificial intelligence model for soil temperature prediction: Novel model. IEEE Access 8:51884\u201351904","journal-title":"IEEE Access"},{"issue":"3","key":"6009_CR43","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S1161-0301(02)00006-0","volume":"17","author":"F Plauborg","year":"2002","unstructured":"Plauborg F (2002) Simple model for 10 cm soil temperature in different soils with short grass. Eur J Agron 17(3):173\u2013179","journal-title":"Eur J Agron"},{"issue":"5","key":"6009_CR44","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s12665-019-8159-6","volume":"78","author":"N Pouladi","year":"2019","unstructured":"Pouladi N, Jafarzadeh AA, Shahbazi F, Ghorbani MA (2019) Design and implementation of a hybrid MLP-FFA model for soil salinity prediction. Environ Earth Sci 78(5):159","journal-title":"Environ Earth Sci"},{"key":"6009_CR45","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.apenergy.2019.05.013","volume":"250","author":"MH Qais","year":"2019","unstructured":"Qais MH, Hasanien HM, Alghuwainem S (2019) Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm. Appl Energy 250:109\u2013117","journal-title":"Appl Energy"},{"issue":"1","key":"6009_CR46","first-page":"290","volume":"4","author":"SN Qasem","year":"2017","unstructured":"Qasem SN, Ebtehaj I, Riahi Madavar H (2017) Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms. J Appl Res Water Wastewater 4(1):290\u2013298","journal-title":"J Appl Res Water Wastewater"},{"key":"6009_CR47","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/j.jhydrol.2016.05.003","volume":"538","author":"J Qi","year":"2016","unstructured":"Qi J, Li S, Li Q, Xing Z, Bourque CPA, Meng FR (2016) A new soil-temperature module for SWAT application in regions with seasonal snow cover. J Hydrol 538:863\u2013877","journal-title":"J Hydrol"},{"key":"6009_CR48","doi-asserted-by":"crossref","first-page":"60314","DOI":"10.1109\/ACCESS.2020.2979927","volume":"8","author":"H Riahi-Madvar","year":"2020","unstructured":"Riahi-Madvar H, Dehghani M, Parmar KS, Nabipour N, Shamshirband S (2020) Improvements in the explicit estimation of pollutant dispersion coefficient in rivers by subset selection of maximum dissimilarity hybridized with ANFIS-firefly algorithm (FFA). IEEE Access 8:60314\u201360337","journal-title":"IEEE Access"},{"key":"6009_CR49","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.still.2017.08.012","volume":"175","author":"S Samadianfard","year":"2018","unstructured":"Samadianfard S, Asadi E, Jarhan S, Kazemi H, Kheshtgar S, Kisi O, Manaf AA (2018a) Wavelet neural networks and gene expression programming models to predict short-term soil temperature at different depths. Soil Tillage Res 175:37\u201350","journal-title":"Soil Tillage Res"},{"issue":"4","key":"6009_CR50","first-page":"465","volume":"5","author":"S Samadianfard","year":"2018","unstructured":"Samadianfard S, Ghorbani MA, Mohammadi B (2018b) Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled-hybrid firefly optimizer algorithm. Inform Process Agric 5(4):465\u2013476","journal-title":"Inform Process Agric"},{"key":"6009_CR51","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.geoderma.2018.05.030","volume":"330","author":"H Sanikhani","year":"2018","unstructured":"Sanikhani H, Deo RC, Yaseen ZM, Eray O, Kisi O (2018) Non-tuned data intelligent model for soil temperature estimation: a new approach. Geoderma 330:52\u201364","journal-title":"Geoderma"},{"issue":"1","key":"6009_CR52","doi-asserted-by":"crossref","first-page":"217","DOI":"10.2166\/wcc.2018.003","volume":"11","author":"A Seifi","year":"2020","unstructured":"Seifi A, Riahi H (2020) Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran. J Water Clim Change 11(1):217\u2013240","journal-title":"J Water Clim Change"},{"key":"6009_CR53","doi-asserted-by":"crossref","first-page":"105418","DOI":"10.1016\/j.compag.2020.105418","volume":"173","author":"A Seifi","year":"2020","unstructured":"Seifi A, Soroush F (2020) Pan evaporation estimation and derivation of explicit optimized equations by novel hybrid meta-heuristic ANN based methods in different climates of Iran. Comput Electron Agric 173:105418","journal-title":"Comput Electron Agric"},{"key":"6009_CR54","doi-asserted-by":"crossref","first-page":"124977","DOI":"10.1016\/j.jhydrol.2020.124977","volume":"587","author":"A Seifi","year":"2020","unstructured":"Seifi A, Ehteram M, Soroush F (2020) Uncertainties of instantaneous influent flow predictions by intelligence models hybridized with multi-objective shark smell optimization algorithm. J Hydrol 587:124977","journal-title":"J Hydrol"},{"issue":"10","key":"6009_CR55","doi-asserted-by":"crossref","first-page":"4023","DOI":"10.3390\/su12104023","volume":"12","author":"A Seifi","year":"2020","unstructured":"Seifi A, Ehteram M, Singh VP, Mosavi A (2020b) Modeling and uncertainty analysis of groundwater level using six evolutionary optimization algorithms hybridized with ANFIS, SVM, and ANN. Sustainability 12(10):4023","journal-title":"Sustainability"},{"key":"6009_CR56","doi-asserted-by":"crossref","first-page":"114292","DOI":"10.1016\/j.enconman.2021.114292","volume":"241","author":"A Seifi","year":"2021","unstructured":"Seifi A, Ehteram M, Dehghani M (2021) A robust integrated Bayesian multi-model uncertainty estimation framework (IBMUEF) for quantifying the uncertainty of hybrid meta-heuristic in global horizontal irradiation predictions. Energy Conv Manag 241:114292","journal-title":"Energy Conv Manag"},{"issue":"1","key":"6009_CR57","first-page":"939","volume":"14","author":"S Shamshirband","year":"2020","unstructured":"Shamshirband S, Esmaeilbeiki F, Zarehaghi D, Neyshabouri M, Samadianfard S, Ghorbani MA, Chau KW (2020) Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths. Eng Appl Comput Fluid Mech 14(1):939\u2013953","journal-title":"Eng Appl Comput Fluid Mech"},{"key":"6009_CR58","doi-asserted-by":"crossref","first-page":"125468","DOI":"10.1016\/j.jhydrol.2020.125468","volume":"591","author":"A Sharafati","year":"2020","unstructured":"Sharafati A, Asadollah SBHS, Neshat A (2020) A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. J Hydrol 591:125468","journal-title":"J Hydrol"},{"issue":"3","key":"6009_CR59","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1080\/24749508.2019.1610841","volume":"4","author":"P Sihag","year":"2020","unstructured":"Sihag P, Esmaeilbeiki F, Singh B, Pandhiani SM (2020) Model-based soil temperature estimation using climatic parameters: the case of Azerbaijan Province Iran. Geol Ecol Landscapes 4(3):203\u2013215","journal-title":"Geol Ecol Landscapes"},{"issue":"11","key":"6009_CR60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-018-3614-3","volume":"11","author":"A Singh","year":"2018","unstructured":"Singh A, Malik A, Kumar A, Kisi O (2018a) Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test. Arab J Geosci 11(11):1\u201312","journal-title":"Arab J Geosci"},{"key":"6009_CR61","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.compag.2018.04.019","volume":"150","author":"VK Singh","year":"2018","unstructured":"Singh VK, Singh BP, Kisi O, Kushwaha DP (2018b) Spatial and multi-depth temporal soil temperature assessment by assimilating satellite imagery, artificial intelligence and regression based models in arid area. Comput Electron Agric 150:205\u2013219","journal-title":"Comput Electron Agric"},{"key":"6009_CR62","doi-asserted-by":"crossref","first-page":"100781","DOI":"10.1016\/j.csite.2020.100781","volume":"22","author":"SE Sofyan","year":"2020","unstructured":"Sofyan SE, Hu E, Kotousov A, Riayatsyah TMI (2020) A new approach to modelling of seasonal soil temperature fluctuations and their impact on the performance of a shallow borehole heat exchanger. Case Stud Thermal Eng 22:100781","journal-title":"Case Stud Thermal Eng"},{"issue":"13","key":"6009_CR63","doi-asserted-by":"publisher","first-page":"5374","DOI":"10.3390\/su12135374","volume":"12","author":"S Stajkowski","year":"2020","unstructured":"Stajkowski S, Kumar D, Samui P, Bonakdari H, Gharabaghi B (2020) Genetic-algorithm-optimized sequential model for water temperature prediction. Sustainability 12(13):5374. https:\/\/doi.org\/10.3390\/su12135374","journal-title":"Sustainability"},{"key":"6009_CR64","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jhydrol.2015.12.045","volume":"534","author":"M Sun","year":"2016","unstructured":"Sun M, Zhang X, Huo Z, Feng S, Huang G, Mao X (2016) Uncertainty and sensitivity assessments of an agricultural\u2013hydrological model (RZWQM2) using the GLUE method. J Hydrol 534:19\u201330","journal-title":"J Hydrol"},{"key":"6009_CR65","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.molstruc.2018.10.040","volume":"1178","author":"HA Tayebi","year":"2019","unstructured":"Tayebi HA, Ghanei M, Aghajani K, Zohrevandi M (2019) Modeling of reactive orange 16 dye removal from aqueous media by mesoporous silica\/crosslinked polymer hybrid using RBF, MLP and GMDH neural network models. J Mol Struct 1178:514\u2013523","journal-title":"J Mol Struct"},{"issue":"2","key":"6009_CR66","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S0169-7439(99)00056-8","volume":"50","author":"B Walczak","year":"2000","unstructured":"Walczak B, Massart DL (2000) Local modelling with radial basis function networks. Chemom Intell Lab Syst 50(2):179\u2013198","journal-title":"Chemom Intell Lab Syst"},{"key":"6009_CR67","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.energy.2018.07.004","volume":"160","author":"L Xing","year":"2018","unstructured":"Xing L, Li L, Gong J, Ren C, Liu J, Chen H (2018) Daily soil temperatures predictions for various climates in United States using data-driven model. Energy 160:430\u2013440","journal-title":"Energy"},{"issue":"2","key":"6009_CR68","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","volume":"2","author":"XS Yang","year":"2010","unstructured":"Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Insp Comput 2(2):78\u201384","journal-title":"Int J Bio-Insp Comput"},{"key":"6009_CR69","doi-asserted-by":"crossref","unstructured":"Yang XS (2012) Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240\u2013249). Springer, Berlin, Heidelberg.","DOI":"10.1007\/978-3-642-32894-7_27"},{"key":"6009_CR70","doi-asserted-by":"crossref","first-page":"105636","DOI":"10.1016\/j.compag.2020.105636","volume":"176","author":"M Zeynoddin","year":"2020","unstructured":"Zeynoddin M, Ebtehaj I, Bonakdari H (2020) Development of a linear based stochastic model for daily soil temperature prediction: one step forward to sustainable agriculture. Comput Electron Agric 176:105636","journal-title":"Comput Electron Agric"},{"key":"6009_CR71","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.jhydrol.2014.11.059","volume":"521","author":"FB Zhang","year":"2015","unstructured":"Zhang FB, Wang ZL, Yang MY (2015) Assessing the applicability of the Taguchi design method to an interrill erosion study. J Hydrol 521:65\u201373","journal-title":"J Hydrol"},{"key":"6009_CR72","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.renene.2018.11.061","volume":"134","author":"X Zhao","year":"2019","unstructured":"Zhao X, Wang C, Su J, Wang J (2019) Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system. Renew Energy 134:681\u2013697","journal-title":"Renew Energy"},{"issue":"2","key":"6009_CR73","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.undsp.2019.12.002","volume":"6","author":"G Zheng","year":"2020","unstructured":"Zheng G, Zhang W, Zhang W, Zhou H, Yang P (2020) Neural network and support vector machine models for the prediction of the liquefaction-induced uplift displacement of tunnels. Underground Space 6(2):126\u2013133","journal-title":"Underground Space"},{"issue":"5","key":"6009_CR74","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s12517-016-2379-9","volume":"9","author":"H Zolfaghari","year":"2016","unstructured":"Zolfaghari H, Masoompour J, Yeganefar M, Akbary M (2016) Studying spatial and temporal changes of aridity in Iran. Arab J Geosci 9(5):375","journal-title":"Arab J Geosci"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06009-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-021-06009-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06009-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T14:21:05Z","timestamp":1670250065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-021-06009-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,12]]},"references-count":74,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["6009"],"URL":"https:\/\/doi.org\/10.1007\/s00500-021-06009-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-285852\/v1","asserted-by":"object"}]},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,12]]},"assertion":[{"value":"26 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2021","order":2,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors have given consent to their contribution.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors have agreed with the content and all have given explicit consent to publish.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}]}}