{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:49:08Z","timestamp":1775771348469,"version":"3.50.1"},"reference-count":95,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"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":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s12145-024-01616-9","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T10:21:33Z","timestamp":1733307693000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep learning approaches for short-crop reference evapotranspiration estimation: a case study in Southeastern Australia"],"prefix":"10.1007","volume":"18","author":[{"given":"Uaktho","family":"Baishnab","sequence":"first","affiliation":[]},{"given":"Md. Sahadat","family":"Hossen Sajib","sequence":"additional","affiliation":[]},{"given":"Ashraful","family":"Islam","sequence":"additional","affiliation":[]},{"given":"Shangida","family":"Akter","sequence":"additional","affiliation":[]},{"given":"Atik","family":"Hasan","sequence":"additional","affiliation":[]},{"given":"Tonmoy","family":"Roy","sequence":"additional","affiliation":[]},{"given":"Pobithra","family":"Das","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"1616_CR1","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.jhydrol.2015.04.073","volume":"527","author":"SS Abdullah","year":"2015","unstructured":"Abdullah SS, Malek MA, Abdullah NS, Kisi O, Yap KS (2015) Extreme Learning machines: a new approach for prediction of reference evapotranspiration. J Hydrol (Amst) 527:184\u2013195. https:\/\/doi.org\/10.1016\/j.jhydrol.2015.04.073","journal-title":"J Hydrol (Amst)"},{"key":"1616_CR2","doi-asserted-by":"crossref","unstructured":"Adnan M, Ahsan Latif M, Nazir M (2017) Estimating Evapotranspiration Using Mach Learn Techniques, www.ijacsa.thesai.org","DOI":"10.14569\/IJACSA.2017.080915"},{"key":"1616_CR3","doi-asserted-by":"publisher","first-page":"4177","DOI":"10.5194\/hess-17-4177-2013","volume":"17","author":"AM Ukkola","year":"2013","unstructured":"Ukkola AM, Prentice IC (2013) A worldwide analysis of trends in water-balance evapotranspiration. Hydrol Earth Syst Sci 17:4177\u20134187. https:\/\/doi.org\/10.5194\/hess-17-4177-2013","journal-title":"Hydrol Earth Syst Sci"},{"key":"1616_CR4","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.jhydrol.2005.07.003","volume":"319","author":"TG Huntington","year":"2006","unstructured":"Huntington TG (2006) Evidence for intensification of the global water cycle: review and synthesis. J Hydrol (Amst) 319:83\u201395. https:\/\/doi.org\/10.1016\/j.jhydrol.2005.07.003","journal-title":"J Hydrol (Amst)"},{"key":"1616_CR5","doi-asserted-by":"publisher","unstructured":"Wang K, Dickinson RE (2012) A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev Geophys 50. https:\/\/doi.org\/10.1029\/2011RG000373","DOI":"10.1029\/2011RG000373"},{"key":"1616_CR6","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.jhydrol.2016.02.053","volume":"536","author":"Y Feng","year":"2016","unstructured":"Feng Y, Cui N, Zhao L, Hu X, Gong D (2016) Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. J Hydrol (Amst) 536:376\u2013383. https:\/\/doi.org\/10.1016\/j.jhydrol.2016.02.053","journal-title":"J Hydrol (Amst)"},{"key":"1616_CR7","doi-asserted-by":"publisher","unstructured":"Wu L, Huang G, Fan J, Ma X, Zhou H, Zeng W (2020) Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction. Comput Electron Agric 168. https:\/\/doi.org\/10.1016\/j.compag.2019.105115","DOI":"10.1016\/j.compag.2019.105115"},{"key":"1616_CR8","doi-asserted-by":"publisher","DOI":"10.1061\/9780784414057","author":"ME Jensen","year":"2016","unstructured":"Jensen ME, Allen RG (2016) Evaporation, evapotranspiration, and irrigation water requirements. Am Soc Civil Eng (ASCE). https:\/\/doi.org\/10.1061\/9780784414057","journal-title":"Am Soc Civil Eng (ASCE)"},{"key":"1616_CR9","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.agwat.2019.03.015","volume":"217","author":"F Granata","year":"2019","unstructured":"Granata F (2019) Evapotranspiration evaluation models based on machine learning algorithms\u2014A comparative study. Agric Water Manag 217:303\u2013315. https:\/\/doi.org\/10.1016\/j.agwat.2019.03.015","journal-title":"Agric Water Manag"},{"key":"1616_CR10","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.agrformet.2013.09.003","volume":"184","author":"D Kool","year":"2014","unstructured":"Kool D, Agam N, Lazarovitch N, Heitman JL, Sauer TJ, Ben-Gal A (2014) A review of approaches for evapotranspiration partitioning. Agric Meteorol 184:56\u201370. https:\/\/doi.org\/10.1016\/j.agrformet.2013.09.003","journal-title":"Agric Meteorol"},{"key":"1616_CR11","doi-asserted-by":"publisher","first-page":"4960","DOI":"10.1111\/gcb.14378","volume":"24","author":"XR Li","year":"2018","unstructured":"Li XR, Jia RL, Zhang ZS, Zhang P, Hui R (2018) Hydrological response of biological soil crusts to global warming: a ten-year simulative study. Glob Chang Biol 24:4960\u20134971. https:\/\/doi.org\/10.1111\/gcb.14378","journal-title":"Glob Chang Biol"},{"key":"1616_CR12","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1080\/19942060.2018.1482476","volume":"12","author":"R Moazenzadeh","year":"2018","unstructured":"Moazenzadeh R, Mohammadi B, Shamshirband S, Chau KW (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comput Fluid Mech 12:584\u2013597. https:\/\/doi.org\/10.1080\/19942060.2018.1482476","journal-title":"Eng Appl Comput Fluid Mech"},{"key":"1616_CR13","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511535680","volume-title":"Ecohydrology: Darwinian Expression of Vegetation Form and Function","author":"PS Eagleson","year":"2002","unstructured":"Eagleson PS (2002) Ecohydrology: darwinian expression of vegetation form and function. Cambridge University Press"},{"key":"1616_CR14","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.agrformet.2018.08.019","volume":"263","author":"J Fan","year":"2018","unstructured":"Fan J, Yue W, Wu L, Zhang F, Cai H, Wang X, Lu X, Xiang Y (2018) Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agric Meteorol 263:225\u2013241. https:\/\/doi.org\/10.1016\/j.agrformet.2018.08.019","journal-title":"Agric Meteorol"},{"key":"1616_CR15","doi-asserted-by":"publisher","unstructured":"Bai Y, Zhang S, Bhattarai N, Mallick K, Liu Q, Tang L, Im J, Guo L, Zhang J (2021) On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient. Agric Meteorol 298\u2013299. https:\/\/doi.org\/10.1016\/j.agrformet.2020.108308","DOI":"10.1016\/j.agrformet.2020.108308"},{"key":"1616_CR16","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.5194\/hess-21-1809-2017","volume":"21","author":"M Hirschi","year":"2017","unstructured":"Hirschi M, Michel D, Lehner I, Seneviratne SI (2017) A site-level comparison of lysimeter and eddy covariance flux measurements of evapotranspiration. Hydrol Earth Syst Sci 21:1809\u20131825. https:\/\/doi.org\/10.5194\/hess-21-1809-2017","journal-title":"Hydrol Earth Syst Sci"},{"key":"1616_CR17","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1002\/wat2.1168","volume":"3","author":"K Zhang","year":"2016","unstructured":"Zhang K, Kimball JS, Running SW (2016) A review of remote sensing based actual evapotranspiration estimation. Wiley Interdisciplinary Reviews: Water 3:834\u2013853. https:\/\/doi.org\/10.1002\/wat2.1168","journal-title":"Wiley Interdisciplinary Reviews: Water"},{"key":"1616_CR18","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/s10712-010-9102-2","volume":"31","author":"EP Glenn","year":"2010","unstructured":"Glenn EP, Nagler PL, Huete AR (2010) Vegetation Index methods for estimating Evapotranspiration by Remote Sensing. Surv Geophys 31:531\u2013555. https:\/\/doi.org\/10.1007\/s10712-010-9102-2","journal-title":"Surv Geophys"},{"key":"1616_CR19","doi-asserted-by":"publisher","first-page":"70","DOI":"10.3390\/s8010070","volume":"8","author":"WW Verstraeten","year":"2008","unstructured":"Verstraeten WW, Veroustraete F, Feyen J (2008) Assessment of Evapotranspiration and Soil Moisture Content across different scales of Observation. Sensors 8:70\u2013117. www.mdpi.org\/sensors","journal-title":"Sensors"},{"key":"1616_CR20","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.agrformet.2012.10.002","volume":"169","author":"A Polhamus","year":"2013","unstructured":"Polhamus A, Fisher JB, Tu KP (2013) What controls the error structure in evapotranspiration models? Agric Meteorol 169:12\u201324. https:\/\/doi.org\/10.1016\/j.agrformet.2012.10.002","journal-title":"Agric Meteorol"},{"key":"1616_CR21","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1175\/JHM628.1","volume":"8","author":"G Abramowitz","year":"2007","unstructured":"Abramowitz G, Pitman A, Gupta H, Kowalczyk E, Wang Y (2007) Systematic bias in land surface models. J Hydrometeorol 8:989\u20131001. https:\/\/doi.org\/10.1175\/JHM628.1","journal-title":"J Hydrometeorol"},{"key":"1616_CR22","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.agrformet.2011.04.008","volume":"153","author":"C Br\u00fcmmer","year":"2012","unstructured":"Br\u00fcmmer C, Black TA, Jassal RS, Grant NJ, Spittlehouse DL, Chen B, Nesic Z, Amiro BD, Arain MA, Barr AG, Bourque CPA, Coursolle C, Dunn AL, Flanagan LB, Humphreys ER, Lafleur PM, Margolis HA, McCaughey JH, Wofsy SC (2012) How climate and vegetation type influence evapotranspiration and water use efficiency in Canadian forest, peatland and grassland ecosystems. Agric Meteorol 153:14\u201330. https:\/\/doi.org\/10.1016\/j.agrformet.2011.04.008","journal-title":"Agric Meteorol"},{"key":"1616_CR23","doi-asserted-by":"publisher","unstructured":"Williams CA, Reichstein M, Buchmann N, Baldocchi D, Beer C, Schwalm C, Wohlfahrt G, Hasler N, Bernhofer C, Foken T, Papale D, Schymanski S, Schaefer K (2012) Climate and vegetation controls on the surface water balance: synthesis of evapotranspiration measured across a global network of flux towers. Water Resour Res 48. https:\/\/doi.org\/10.1029\/2011WR011586","DOI":"10.1029\/2011WR011586"},{"key":"1616_CR24","doi-asserted-by":"publisher","first-page":"96","DOI":"10.13031\/2013.26773","volume":"1","author":"H George","year":"1985","unstructured":"George H, Hargreaves ZA, Samani (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96\u201399. https:\/\/doi.org\/10.13031\/2013.26773","journal-title":"Appl Eng Agric"},{"key":"1616_CR25","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.jhydrol.2016.10.022","volume":"543","author":"S Li","year":"2016","unstructured":"Li S, Kang S, Zhang L, Zhang J, Du T, Tong L, Ding R (2016) Evaluation of six potential evapotranspiration models for estimating crop potential and actual evapotranspiration in arid regions. J Hydrol (Amst) 543:450\u2013461. https:\/\/doi.org\/10.1016\/j.jhydrol.2016.10.022","journal-title":"J Hydrol (Amst)"},{"key":"1616_CR26","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.compag.2015.02.010","volume":"113","author":"M Goci\u0107","year":"2015","unstructured":"Goci\u0107 M, Motamedi S, Shamshirband S, Petkovi\u0107 D, Ch S, Hashim R, Arif M (2015) Soft computing approaches for forecasting reference evapotranspiration. Comput Electron Agric 113:164\u2013173. https:\/\/doi.org\/10.1016\/j.compag.2015.02.010","journal-title":"Comput Electron Agric"},{"key":"1616_CR27","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s11269-013-0474-1","volume":"28","author":"H Citakoglu","year":"2014","unstructured":"Citakoglu H, Cobaner M, Haktanir T, Kisi O (2014) Estimation of Monthly Mean Reference Evapotranspiration in Turkey. Water Resour Manage 28:99\u2013113. https:\/\/doi.org\/10.1007\/s11269-013-0474-1","journal-title":"Water Resour Manage"},{"key":"1616_CR28","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.agwat.2016.02.026","volume":"169","author":"O Kisi","year":"2016","unstructured":"Kisi O (2016) Modeling reference evapotranspiration using three different heuristic regression approaches. Agric Water Manag 169:162\u2013172. https:\/\/doi.org\/10.1016\/j.agwat.2016.02.026","journal-title":"Agric Water Manag"},{"key":"1616_CR29","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/0022-1694(84)90085-4","volume":"72","author":"C Riou","year":"1984","unstructured":"Riou C (1984) Experimental study of potential evapotranspiration (PET) in Central Africa. J Hydrol (Amst) 72:275\u2013288. https:\/\/doi.org\/10.1016\/0022-1694(84)90085-4","journal-title":"J Hydrol (Amst)"},{"key":"1616_CR30","doi-asserted-by":"publisher","unstructured":"Liu C, Cui N, Gong D, Hu X, Feng Y (2020) Evaluation of seasonal evapotranspiration of winter wheat in humid region of East China using large-weighted lysimeter and three models. J Hydrol (Amst) 590. https:\/\/doi.org\/10.1016\/j.jhydrol.2020.125388","DOI":"10.1016\/j.jhydrol.2020.125388"},{"key":"1616_CR31","doi-asserted-by":"publisher","unstructured":"Allen RG, Pruitt WO (1991) FAO-24 Reference Evapotranspiration Factors, Journal of Irrigation and Drainage Engineering 117 758\u2013773. https:\/\/doi.org\/10.1061\/(ASCE)0733-9437(1991)117:5(758)","DOI":"10.1061\/(ASCE)0733-9437"},{"key":"1616_CR32","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.jhydrol.2017.08.006","volume":"553","author":"M Rezaie-balf","year":"2017","unstructured":"Rezaie-balf M, Naganna SR, Ghaemi A, Deka PC (2017) Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting. J Hydrol (Amst) 553:356\u2013373. https:\/\/doi.org\/10.1016\/j.jhydrol.2017.08.006","journal-title":"J Hydrol (Amst)"},{"key":"1616_CR33","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.compag.2017.05.036","volume":"140","author":"L Wang","year":"2017","unstructured":"Wang L, Niu Z, Kisi O, Li C, Yu D (2017) Pan evaporation modeling using four different heuristic approaches. Comput Electron Agric 140:203\u2013213. https:\/\/doi.org\/10.1016\/j.compag.2017.05.036","journal-title":"Comput Electron Agric"},{"key":"1616_CR34","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.agwat.2018.07.039","volume":"210","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Gong Y, Wang Z (2018) Accessible remote sensing data based reference evapotranspiration estimation modelling. Agric Water Manag 210:59\u201369. https:\/\/doi.org\/10.1016\/j.agwat.2018.07.039","journal-title":"Agric Water Manag"},{"key":"1616_CR35","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.agwat.2018.07.023","volume":"209","author":"SS Anapalli","year":"2018","unstructured":"Anapalli SS, Fisher DK, Reddy KN, Wagle P, Gowda PH, Sui R (2018) Quantifying soybean evapotranspiration using an eddy covariance approach. Agric Water Manag 209:228\u2013239. https:\/\/doi.org\/10.1016\/j.agwat.2018.07.023","journal-title":"Agric Water Manag"},{"key":"1616_CR36","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.agwat.2018.07.041","volume":"209","author":"G Pozn\u00edkov\u00e1","year":"2018","unstructured":"Pozn\u00edkov\u00e1 G, Fischer M, van Kesteren B, Ors\u00e1g M, Hlavinka P, \u017dalud Z, Trnka M (2018) Quantifying turbulent energy fluxes and evapotranspiration in agricultural field conditions: a comparison of micrometeorological methods. Agric Water Manag 209:249\u2013263. https:\/\/doi.org\/10.1016\/j.agwat.2018.07.041","journal-title":"Agric Water Manag"},{"key":"1616_CR37","doi-asserted-by":"publisher","first-page":"3032","DOI":"10.1002\/hyp.13252","volume":"32","author":"R Chai","year":"2018","unstructured":"Chai R, Sun S, Chen H, Zhou S (2018) Changes in reference evapotranspiration over China during 1960\u20132012: attributions and relationships with atmospheric circulation. Hydrol Process 32:3032\u20133048. https:\/\/doi.org\/10.1002\/hyp.13252","journal-title":"Hydrol Process"},{"key":"1616_CR38","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.compag.2018.07.029","volume":"152","author":"D Tang","year":"2018","unstructured":"Tang D, Feng Y, Gong D, Hao W, Cui N (2018) Evaluation of artificial intelligence models for actual crop evapotranspiration modeling in mulched and non-mulched maize croplands. Comput Electron Agric 152:375\u2013384. https:\/\/doi.org\/10.1016\/j.compag.2018.07.029","journal-title":"Comput Electron Agric"},{"key":"1616_CR39","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.agwat.2018.07.016","volume":"209","author":"A Negm","year":"2018","unstructured":"Negm A, Minacapilli M, Provenzano G, Downscaling of American National Aeronautics and Space (2018) Administration (NASA) daily air temperature in Sicily, Italy, and effects on crop reference evapotranspiration. Agric Water Manag 209:151\u2013162. https:\/\/doi.org\/10.1016\/j.agwat.2018.07.016","journal-title":"Agric Water Manag"},{"key":"1616_CR40","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.agwat.2016.08.025","volume":"180","author":"M Valipour","year":"2017","unstructured":"Valipour M, Gholami Sefidkouhi MA, Raeini\u2009\u2013\u2009Sarjaz M (2017) Selecting the best model to estimate potential evapotranspiration with respect to climate change and magnitudes of extreme events. Agric Water Manag 180:50\u201360. https:\/\/doi.org\/10.1016\/j.agwat.2016.08.025","journal-title":"Agric Water Manag"},{"key":"1616_CR41","doi-asserted-by":"publisher","first-page":"2618","DOI":"10.1002\/2016WR020175","volume":"53","author":"JB Fisher","year":"2017","unstructured":"Fisher JB, Melton F, Middleton E, Hain C, Anderson M, Allen R, McCabe MF, Hook S, Baldocchi D, Townsend PA, Kilic A, Tu K, Miralles DD, Perret J, Lagouarde JP, Waliser D, Purdy AJ, French A, Schimel D, Famiglietti JS, Stephens G, Wood EF (2017) The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour Res 53:2618\u20132626. https:\/\/doi.org\/10.1002\/2016WR020175","journal-title":"Water Resour Res"},{"key":"1616_CR42","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.agwat.2017.08.003","volume":"193","author":"Y Feng","year":"2017","unstructured":"Feng Y, Cui N, Gong D, Zhang Q, Zhao L (2017) Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agric Water Manag 193:163\u2013173. https:\/\/doi.org\/10.1016\/j.agwat.2017.08.003","journal-title":"Agric Water Manag"},{"key":"1616_CR43","doi-asserted-by":"publisher","unstructured":"Ferreira LB, da Cunha FF (2020) New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning. Agric Water Manag 234. https:\/\/doi.org\/10.1016\/j.agwat.2020.106113","DOI":"10.1016\/j.agwat.2020.106113"},{"key":"1616_CR44","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1177\/0309133312444943","volume":"36","author":"RJ Abrahart","year":"2012","unstructured":"Abrahart RJ, Anctil F, Coulibaly P, Dawson CW, Mount NJ, See LM, Shamseldin AY, Solomatine DP, Toth E, Wilby RL (2012) Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Prog Phys Geogr 36:480\u2013513. https:\/\/doi.org\/10.1177\/0309133312444943","journal-title":"Prog Phys Geogr"},{"key":"1616_CR45","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.compag.2016.05.018","volume":"127","author":"B Keshtegar","year":"2016","unstructured":"Keshtegar B, Piri J, Kisi O (2016) A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method. Comput Electron Agric 127:120\u2013130. https:\/\/doi.org\/10.1016\/j.compag.2016.05.018","journal-title":"Comput Electron Agric"},{"key":"1616_CR46","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s00704-015-1624-6","volume":"127","author":"MR Kousari","year":"2017","unstructured":"Kousari MR, Hosseini ME, Ahani H, Hakimelahi H (2017) Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities. Theor Appl Climatol 127:361\u2013380. https:\/\/doi.org\/10.1007\/s00704-015-1624-6","journal-title":"Theor Appl Climatol"},{"key":"1616_CR47","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.advwatres.2008.10.005","volume":"32","author":"A Moghaddamnia","year":"2009","unstructured":"Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32:88\u201397. https:\/\/doi.org\/10.1016\/j.advwatres.2008.10.005","journal-title":"Adv Water Resour"},{"key":"1616_CR48","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.agrformet.2015.10.011","volume":"216","author":"S Park","year":"2016","unstructured":"Park S, Im J, Jang E, Rhee J (2016) Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agric Meteorol 216:157\u2013169. https:\/\/doi.org\/10.1016\/j.agrformet.2015.10.011","journal-title":"Agric Meteorol"},{"key":"1616_CR49","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.ecolmodel.2012.03.001","volume":"240","author":"C Crisci","year":"2012","unstructured":"Crisci C, Ghattas B, Perera G (2012) A review of supervised machine learning algorithms and their applications to ecological data. Ecol Modell 240:113\u2013122. https:\/\/doi.org\/10.1016\/j.ecolmodel.2012.03.001","journal-title":"Ecol Modell"},{"key":"1616_CR50","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.renene.2016.12.095","volume":"105","author":"C Voyant","year":"2017","unstructured":"Voyant C, Notton G, Kalogirou S, Nivet ML, Paoli C, Motte F, Fouilloy A (2017) Machine learning methods for solar radiation forecasting: a review. Renew Energy 105:569\u2013582. https:\/\/doi.org\/10.1016\/j.renene.2016.12.095","journal-title":"Renew Energy"},{"key":"1616_CR51","doi-asserted-by":"publisher","first-page":"2590","DOI":"10.1080\/02626667.2016.1142667","volume":"61","author":"VZ Antonopoulos","year":"2016","unstructured":"Antonopoulos VZ, Gianniou SK, Antonopoulos AV (2016) Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece. Hydrol Sci J 61:2590\u20132599. https:\/\/doi.org\/10.1080\/02626667.2016.1142667","journal-title":"Hydrol Sci J"},{"key":"1616_CR52","doi-asserted-by":"publisher","first-page":"3834","DOI":"10.1002\/joc.4249","volume":"35","author":"O Kisi","year":"2015","unstructured":"Kisi O, Sanikhani H (2015) Modelling long-term monthly temperatures by several data-driven methods using geographical inputs. Int J Climatol 35:3834\u20133846. https:\/\/doi.org\/10.1002\/joc.4249","journal-title":"Int J Climatol"},{"key":"1616_CR53","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.compag.2017.05.002","volume":"139","author":"S Mehdizadeh","year":"2017","unstructured":"Mehdizadeh S, Behmanesh J, Khalili K, Using MARS (2017) SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Comput Electron Agric 139:103\u2013114. https:\/\/doi.org\/10.1016\/j.compag.2017.05.002","journal-title":"Comput Electron Agric"},{"key":"1616_CR54","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1007\/s00704-016-1943-2","volume":"130","author":"N Misaghian","year":"2017","unstructured":"Misaghian N, Shamshirband S, Petkovi\u0107 D, Gocic M, Mohammadi K (2017) Predicting the reference evapotranspiration based on tensor decomposition. Theor Appl Climatol 130:1099\u20131109. https:\/\/doi.org\/10.1007\/s00704-016-1943-2","journal-title":"Theor Appl Climatol"},{"key":"1616_CR55","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s00704-015-1522-y","volume":"125","author":"D Petkovi\u0107","year":"2016","unstructured":"Petkovi\u0107 D, Gocic M, Shamshirband S, Qasem SN, Trajkovic S (2016) Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration. Theor Appl Climatol 125:555\u2013563. https:\/\/doi.org\/10.1007\/s00704-015-1522-y","journal-title":"Theor Appl Climatol"},{"key":"1616_CR56","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/s00271-012-0332-6","volume":"31","author":"H Tabari","year":"2013","unstructured":"Tabari H, Martinez C, Ezani A, Hosseinzadeh P, Talaee (2013) Applicability of support vector machines and adaptive neurofuzzy inference system for modeling potato crop evapotranspiration. Irrig Sci 31:575\u2013588. https:\/\/doi.org\/10.1007\/s00271-012-0332-6","journal-title":"Irrig Sci"},{"key":"1616_CR57","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1134\/S0097807816020172","volume":"43","author":"MA Yassin","year":"2016","unstructured":"Yassin MA, Alazba AA, Mattar MA (2016) Comparison between gene expression programming and traditional models for estimating evapotranspiration under hyper arid conditions. Water Resour 43:412\u2013427. https:\/\/doi.org\/10.1134\/S0097807816020172","journal-title":"Water Resour"},{"key":"1616_CR58","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1016\/j.jhydrol.2019.03.028","volume":"572","author":"LB Ferreira","year":"2019","unstructured":"Ferreira LB, da Cunha FF, de Oliveira RA, Fernandes Filho EI (2019) Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM \u2013 A new approach. J Hydrol (Amst) 572:556\u2013570. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.03.028","journal-title":"J Hydrol (Amst)"},{"key":"1616_CR59","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s00271-010-0230-8","volume":"29","author":"M Kumar","year":"2011","unstructured":"Kumar M, Raghuwanshi NS, Singh R (2011) Artificial neural networks approach in evapotranspiration modeling: a review. Irrig Sci 29:11\u201325. https:\/\/doi.org\/10.1007\/s00271-010-0230-8","journal-title":"Irrig Sci"},{"key":"1616_CR60","doi-asserted-by":"publisher","unstructured":"Nourani V, Elkiran G, Abdullahi J (2019) Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements. J Hydrol (Amst) 577. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.123958","DOI":"10.1016\/j.jhydrol.2019.123958"},{"key":"1616_CR61","doi-asserted-by":"publisher","unstructured":"Wu L, Fan J (2019) Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration. PLoS ONE 14. https:\/\/doi.org\/10.1371\/journal.pone.0217520","DOI":"10.1371\/journal.pone.0217520"},{"key":"1616_CR62","doi-asserted-by":"publisher","unstructured":"Fan J, Ma X, Wu L, Zhang F, Yu X, Zeng W (2019) Light gradient boosting machine: an efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data. Agric Water Manag 225. https:\/\/doi.org\/10.1016\/j.agwat.2019.105758","DOI":"10.1016\/j.agwat.2019.105758"},{"key":"1616_CR63","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/s00704-016-1888-5","volume":"130","author":"H Kiafar","year":"2017","unstructured":"Kiafar H, Babazadeh H, Marti P, Kisi O, Landeras G, Karimi S, Shiri J (2017) Evaluating the generalizability of GEP models for estimating reference evapotranspiration in distant humid and arid locations. Theor Appl Climatol 130:377\u2013389. https:\/\/doi.org\/10.1007\/s00704-016-1888-5","journal-title":"Theor Appl Climatol"},{"key":"1616_CR64","doi-asserted-by":"publisher","unstructured":"Reis MM, da Silva AJ, Zullo Junior J, Tuffi Santos LD, Azevedo AM, Lopes \u00c9MG (2019) Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data. Comput Electron Agric 165. https:\/\/doi.org\/10.1016\/j.compag.2019.104937","DOI":"10.1016\/j.compag.2019.104937"},{"key":"1616_CR65","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1002\/hyp.7266","volume":"23","author":"M Pal","year":"2009","unstructured":"Pal M, Deswal S (2009) M5 model tree based modelling of reference evapotranspiration. Hydrol Process 23:1437\u20131443. https:\/\/doi.org\/10.1002\/hyp.7266","journal-title":"Hydrol Process"},{"key":"1616_CR66","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1002\/ird.445","volume":"58","author":"E Do\u011fan","year":"2009","unstructured":"Do\u011fan E (2009) Reference evapotranspiration estimation using adaptive neuro-fuzzy inference systems. Irrig Sci 58:617\u2013628. https:\/\/doi.org\/10.1002\/ird.445","journal-title":"Irrig Sci"},{"key":"1616_CR67","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.1007\/s11269-021-02820-8","volume":"35","author":"M Goci\u0107","year":"2021","unstructured":"Goci\u0107 M, Amiri MA (2021) Reference Evapotranspiration Prediction using neural networks and Optimum Time lags. Water Resour Manage 35:1913\u20131926. https:\/\/doi.org\/10.1007\/s11269-021-02820-8","journal-title":"Water Resour Manage"},{"key":"1616_CR68","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1080\/02626667.2020.1830996","volume":"65","author":"M Nagappan","year":"2020","unstructured":"Nagappan M, Gopalakrishnan V, Alagappan M (2020) Prediction of reference evapotranspiration for irrigation scheduling using machine learning. Hydrol Sci J 65:2669\u20132677. https:\/\/doi.org\/10.1080\/02626667.2020.1830996","journal-title":"Hydrol Sci J"},{"key":"1616_CR69","doi-asserted-by":"publisher","first-page":"125252","DOI":"10.1016\/j.jhydrol.2020.125252","volume":"590","author":"M Hossein Kazemi","year":"2020","unstructured":"Hossein Kazemi M, Shiri J, Marti P, Majnooni-Heris A (2020) Assessing temporal data partitioning scenarios for estimating reference evapotranspiration with machine learning techniques in arid regions. J Hydrol (Amst) 590:125252. https:\/\/doi.org\/10.1016\/j.jhydrol.2020.125252","journal-title":"J Hydrol (Amst)"},{"key":"1616_CR70","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compag.2019.03.030","volume":"162","author":"J Shiri","year":"2019","unstructured":"Shiri J, Marti P, Karimi S, Landeras G (2019) Data splitting strategies for improving data driven models for reference evapotranspiration estimation among similar stations. Comput Electron Agric 162:70\u201381. https:\/\/doi.org\/10.1016\/j.compag.2019.03.030","journal-title":"Comput Electron Agric"},{"key":"1616_CR71","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1007\/s12145-020-00477-2","volume":"13","author":"D Hussain","year":"2020","unstructured":"Hussain D, Hussain T, Khan AA, Naqvi SAA, Jamil A (2020) A deep learning approach for hydrological time-series prediction: a case study of Gilgit river basin. Earth Sci Inf 13:915\u2013927. https:\/\/doi.org\/10.1007\/s12145-020-00477-2","journal-title":"Earth Sci Inf"},{"key":"1616_CR72","doi-asserted-by":"publisher","unstructured":"Masrur Ahmed AA, Feng Q, Ghahramani A, Raj N, Yin Z, Yang L (2021) Hybrid Deep Learning for Week-Ahead Evapotranspiration Forecasting, https:\/\/doi.org\/10.21203\/rs.3.rs-424493\/v1","DOI":"10.21203\/rs.3.rs-424493\/v1"},{"key":"1616_CR73","doi-asserted-by":"publisher","unstructured":"Yang X, Zhang Z, CNN-LSTM Model A (2022) Based on a Meta-learning algorithm to Predict Groundwater Level in the Middle and Lower reaches of the Heihe River, China, Water (Basel) 14. 2377. https:\/\/doi.org\/10.3390\/w14152377","DOI":"10.3390\/w14152377"},{"key":"1616_CR74","doi-asserted-by":"publisher","unstructured":"Wu L, Kong C, Hao X, Chen W (2020) A short-term load forecasting Method based on GRU-CNN hybrid neural network model. Math Probl Eng 2020. https:\/\/doi.org\/10.1155\/2020\/1428104","DOI":"10.1155\/2020\/1428104"},{"key":"1616_CR75","doi-asserted-by":"publisher","unstructured":"Ni G, Zhang X, Ni X, Cheng X, Meng X (2023) A WOA-CNN-BiLSTM-based multi-feature classification prediction model for smart grid financial markets. Front Energy Res 11. https:\/\/doi.org\/10.3389\/fenrg.2023.1198855","DOI":"10.3389\/fenrg.2023.1198855"},{"key":"1616_CR76","doi-asserted-by":"publisher","first-page":"60090","DOI":"10.1109\/ACCESS.2020.2982433","volume":"8","author":"M Pan","year":"2020","unstructured":"Pan M, Zhou H, Cao J, Liu Y, Hao J, Li S, Chen CH (2020) Water Level Prediction Model based on GRU and CNN. IEEE Access 8:60090\u201360100. https:\/\/doi.org\/10.1109\/ACCESS.2020.2982433","journal-title":"IEEE Access"},{"key":"1616_CR77","doi-asserted-by":"publisher","first-page":"71805","DOI":"10.1109\/ACCESS.2021.3077703","volume":"9","author":"XH Le","year":"2021","unstructured":"Le XH, Nguyen DH, Jung S, Yeon M, Lee G (2021) Comparison of deep learning techniques for river streamflow forecasting. IEEE Access 9:71805\u201371820. https:\/\/doi.org\/10.1109\/ACCESS.2021.3077703","journal-title":"IEEE Access"},{"key":"1616_CR78","doi-asserted-by":"publisher","first-page":"154722","DOI":"10.1016\/j.scitotenv.2022.154722","volume":"831","author":"AAM Ahmed","year":"2022","unstructured":"Ahmed AAM, Deo RC, Ghahramani A, Feng Q, Raj N, Yin Z, Yang L (2022) New double decomposition deep learning methods for river water level forecasting. Sci Total Environ 831:154722. https:\/\/doi.org\/10.1016\/j.scitotenv.2022.154722","journal-title":"Sci Total Environ"},{"key":"1616_CR79","doi-asserted-by":"publisher","first-page":"113944","DOI":"10.1016\/j.enconman.2021.113944","volume":"234","author":"KU Jaseena","year":"2021","unstructured":"Jaseena KU, Kovoor BC (2021) Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks. Energy Convers Manag 234:113944. https:\/\/doi.org\/10.1016\/j.enconman.2021.113944","journal-title":"Energy Convers Manag"},{"key":"1616_CR80","doi-asserted-by":"publisher","unstructured":"Bian C, He H, Yang S, Huang T (2020) State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture. J Power Sources 449. https:\/\/doi.org\/10.1016\/j.jpowsour.2019.227558","DOI":"10.1016\/j.jpowsour.2019.227558"},{"key":"1616_CR81","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"1616_CR82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"G Montavon","year":"2018","unstructured":"Montavon G, Samek W, M\u00fcller KR (2018) Methods for interpreting and understanding deep neural networks. Digit Signal Processing: Rev J 73:1\u201315. https:\/\/doi.org\/10.1016\/j.dsp.2017.10.011","journal-title":"Digit Signal Processing: Rev J"},{"key":"1616_CR83","doi-asserted-by":"publisher","first-page":"R231","DOI":"10.1016\/j.cub.2019.02.034","volume":"29","author":"N Kriegeskorte","year":"2019","unstructured":"Kriegeskorte N, Golan T (2019) Neural network models and deep learning. Curr Biol 29:R231\u2013R236. https:\/\/doi.org\/10.1016\/j.cub.2019.02.034","journal-title":"Curr Biol"},{"key":"1616_CR84","doi-asserted-by":"publisher","unstructured":"Deng C, Ji X, Rainey C, Zhang J, Lu W (2020) Integrating machine learning with human knowledge. IScience 23. https:\/\/doi.org\/10.1016\/j.isci.2020.101656","DOI":"10.1016\/j.isci.2020.101656"},{"key":"1616_CR85","doi-asserted-by":"publisher","unstructured":"Das P, Kashem A (2024) Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations. Case Stud Constr Mater 20. https:\/\/doi.org\/10.1016\/j.cscm.2023.e02723","DOI":"10.1016\/j.cscm.2023.e02723"},{"key":"1616_CR86","doi-asserted-by":"publisher","unstructured":"Ahmed A, Song W, Zhang Y, Haque MA, Liu X (2023) Hybrid BO-XGBoost and BO-RF models for the Strength Prediction of Self-compacting mortars with Parametric Analysis. Materials 16. https:\/\/doi.org\/10.3390\/ma16124366","DOI":"10.3390\/ma16124366"},{"key":"1616_CR87","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1016\/j.rser.2014.07.117","volume":"39","author":"CA Gueymard","year":"2014","unstructured":"Gueymard CA (2014) A review of validation methodologies and statistical performance indicators for modeled solar radiation data: towards a better bankability of solar projects. Renew Sustain Energy Rev 39:1024\u20131034. https:\/\/doi.org\/10.1016\/j.rser.2014.07.117","journal-title":"Renew Sustain Energy Rev"},{"key":"1616_CR88","doi-asserted-by":"publisher","first-page":"116599","DOI":"10.1016\/j.compstruct.2022.116599","volume":"306","author":"X Shi","year":"2023","unstructured":"Shi X, Yu X, Esmaeili-Falak M (2023) Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation. Compos Struct 306:116599. https:\/\/doi.org\/10.1016\/j.compstruct.2022.116599","journal-title":"Compos Struct"},{"key":"1616_CR89","doi-asserted-by":"publisher","first-page":"5417","DOI":"10.1002\/suco.202200260","volume":"24","author":"MEA Ben Seghier","year":"2023","unstructured":"Ben Seghier MEA, Golafshani EM, Jafari-Asl J, Arashpour M (2023) Metaheuristic\u2010based machine learning modeling of the compressive strength of concrete containing waste glass. Struct Concrete 24:5417\u20135440. https:\/\/doi.org\/10.1002\/suco.202200260","journal-title":"Struct Concrete"},{"key":"1616_CR90","doi-asserted-by":"publisher","unstructured":"Chen Z, Zhu Z, Jiang H, Sun S (2020) Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. J Hydrol (Amst) 591. https:\/\/doi.org\/10.1016\/j.jhydrol.2020.125286","DOI":"10.1016\/j.jhydrol.2020.125286"},{"key":"1616_CR91","doi-asserted-by":"publisher","unstructured":"Ikram RMA, Mostafa RR, Chen Z, Islam ARMT, Kisi O, Kuriqi A (2023) Zounemat-Kermani, Advanced Hybrid Metaheuristic Machine Learning models Application for Reference Crop Evapotranspiration Prediction. Agronomy 13. https:\/\/doi.org\/10.3390\/agronomy13010098","DOI":"10.3390\/agronomy13010098"},{"key":"1616_CR92","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.compag.2018.03.010","volume":"148","author":"X Dou","year":"2018","unstructured":"Dou X, Yang Y (2018) Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems. Comput Electron Agric 148:95\u2013106. https:\/\/doi.org\/10.1016\/j.compag.2018.03.010","journal-title":"Comput Electron Agric"},{"key":"1616_CR93","doi-asserted-by":"crossref","unstructured":"Babaeian E, Paheding S, Siddique N, Devabhaktuni VK, Tuller M (2022) Short-and Mid-Term Forecasts of Actual Evapotranspiration with Deep Learning 2 Short-and Mid-Term Forecasts of Actual Evapotranspiration with Deep","DOI":"10.1016\/j.jhydrol.2022.128078"},{"key":"1616_CR94","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/hydrology8010025","volume":"8","author":"AR Niaghi","year":"2021","unstructured":"Niaghi AR, Hassanijalilian O, Shiri J (2021) Estimation of reference evapotranspiration using spatial and temporal machine learning approaches. Hydrology 8:1\u201315. https:\/\/doi.org\/10.3390\/hydrology8010025","journal-title":"Hydrology"},{"key":"1616_CR95","doi-asserted-by":"publisher","unstructured":"Nandagiri L, Kovoor GM (n.d.) Performance evaluation of reference evapotranspiration equations across a range of Indian climates. https:\/\/doi.org\/10.1061\/ASCE0733-94372006132:3238","DOI":"10.1061\/ASCE0733-94372006132:3238"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01616-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01616-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01616-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T08:07:38Z","timestamp":1745654858000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01616-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"references-count":95,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1616"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01616-9","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"29 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"4"}}