{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:22:57Z","timestamp":1770747777649,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s00521-020-04995-4","type":"journal-article","created":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T16:06:48Z","timestamp":1592410008000},"page":"1547-1557","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A two-step combined algorithm based on NARX neural network and the subsequent prediction of the residues improves prediction accuracy of the greenhouse gases concentrations"],"prefix":"10.1007","volume":"33","author":[{"given":"Alexander","family":"Buevich","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Sergeev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrey","family":"Shichkin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Baglaeva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"key":"4995_CR1","unstructured":"AMAP (2017) Snow, water, ice and permafrost. Summary for policy-makers. In: Arctic monitoring and assessment programme (AMAP), Oslo Norway, 20"},{"key":"4995_CR2","unstructured":"Nullis C (2016) Provisional WMO statement on the status of the global climate in 2016. World Meteorological Organization"},{"key":"4995_CR3","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.gloplacha.2011.03.004","volume":"77","author":"MC Serreze","year":"2011","unstructured":"Serreze MC, Barry RG (2011) Processes and impacts of Arctic amplification: a research synthesis. Glob Planet Chang 77:85\u201396","journal-title":"Glob Planet Chang"},{"key":"4995_CR4","doi-asserted-by":"publisher","first-page":"6629","DOI":"10.1016\/j.atmosenv.2011.09.006","volume":"37","author":"C Borrego","year":"2011","unstructured":"Borrego C, Monteiro A, Pay MT, Ribeiro I, Miranda AI, Basart S, Baldasano JM (2011) How bias-correction can improve air quality forecasts over Portugal. Atmos Environ 37:6629\u20136641","journal-title":"Atmos Environ"},{"key":"4995_CR5","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1080\/02786826.2010.490798","volume":"44","author":"N Chu","year":"2010","unstructured":"Chu N, Kadane JB, Davidson CI (2010) Using statistical regressions to identify factors influencing PM2.5 concentrations: the Pittsburgh supersite as a case study. Aerosol Sci Technol 44:766\u2013774","journal-title":"Aerosol Sci Technol"},{"key":"4995_CR6","doi-asserted-by":"publisher","first-page":"3015","DOI":"10.1016\/j.atmosenv.2010.05.009","volume":"44","author":"WG Cobourn","year":"2010","unstructured":"Cobourn WG (2010) An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations. Atmos Environ 44:3015\u20133023","journal-title":"Atmos Environ"},{"issue":"1","key":"4995_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2307\/1912017","volume":"48","author":"CA Sims","year":"1980","unstructured":"Sims CA (1980) Macroeconomics and reality. Econometrica 48(1):1\u201348","journal-title":"Econometrica"},{"key":"4995_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2019.116988","author":"DA Olson","year":"2019","unstructured":"Olson DA, Riedel TP, Long R, Offenberg JH, Lewandowski M, Kleindienst TE (2019) Time series analysis of wintertime O3 and NOx formation using vector autoregressions. Atmos Environ. https:\/\/doi.org\/10.1016\/j.atmosenv.2019.116988","journal-title":"Atmos Environ"},{"key":"4995_CR9","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.scitotenv.2019.05.360","volume":"684","author":"Y Zhang","year":"2019","unstructured":"Zhang Y (2019) Dynamic effect analysis of meteorological conditions on air pollution: a case study from Beijing. Sci Total Environ 684:178\u2013185","journal-title":"Sci Total Environ"},{"issue":"2","key":"4995_CR10","doi-asserted-by":"publisher","first-page":"923","DOI":"10.15244\/pjoes\/83668","volume":"28","author":"L Wen","year":"2019","unstructured":"Wen L, Zhang X (2019) CO2 emissions in China\u2019s Yangtze River economic zone: a dynamic vector autoregression approach. Pol J Environ Stud 28(2):923\u2013933. https:\/\/doi.org\/10.15244\/pjoes\/83668","journal-title":"Pol J Environ Stud"},{"key":"4995_CR11","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.energy.2018.03.052","volume":"151","author":"B Lin","year":"2018","unstructured":"Lin B, Xu B (2018) Growth of industrial CO2 emissions in Shanghai city: evidence from a dynamic vector autoregression analysis. Energy 151:167\u2013177","journal-title":"Energy"},{"issue":"1","key":"4995_CR12","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s10527-013-9331-z","volume":"47","author":"AV Dmitriev","year":"2013","unstructured":"Dmitriev AV (2013) Time series prediction of morbidity using artificial neural networks. Biomed Eng 47(1):43\u201345","journal-title":"Biomed Eng"},{"key":"4995_CR13","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.envpol.2011.12.018","volume":"163","author":"HJS Fernando","year":"2012","unstructured":"Fernando HJS, Mammarella MC, Grandoni C, Fedele P, Di Marco R, Dimitrova R, Hyde P (2012) Forecasting PM10 in metropolitan areas: efficacy of neural networks. Environ Pollut 163:62\u201367","journal-title":"Environ Pollut"},{"key":"4995_CR14","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s10661-009-0981-y","volume":"166","author":"DD Genc","year":"2010","unstructured":"Genc DD, Yesilyurt C, Tuncel G (2010) Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere. Environ Monit Assess 166:11\u201327","journal-title":"Environ Monit Assess"},{"key":"4995_CR15","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1016\/j.atmosenv.2013.07.072","volume":"79","author":"A Russo","year":"2013","unstructured":"Russo A, Raischel F, Lind P (2013) Air quality prediction using optimal neural networks with stochastic variables. Atmos Environ 79:822\u2013830","journal-title":"Atmos Environ"},{"key":"4995_CR16","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","volume":"50","author":"G Zhang","year":"2003","unstructured":"Zhang G (2003) Time series forecasting using a combined ARIMA and neural network model. Neurocomputing 50:159\u2013175","journal-title":"Neurocomputing"},{"key":"4995_CR17","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.scitotenv.2014.07.051","volume":"496","author":"Q Zhou","year":"2014","unstructured":"Zhou Q, Jiang H, Wang J, Zhou J (2014) A combined model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Sci Total Environ 496:264\u2013274","journal-title":"Sci Total Environ"},{"key":"4995_CR18","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/j.asoc.2019.02.006","volume":"77","author":"X Wenquan","year":"2019","unstructured":"Wenquan X et al (2019) Deep belief network-based AR model for nonlinear time series forecasting. Appl Soft Comput 77:605\u2013621","journal-title":"Appl Soft Comput"},{"key":"4995_CR19","doi-asserted-by":"publisher","DOI":"10.4324\/9780203491683","volume-title":"The analysis of time series: an introduction","author":"C Chatfield","year":"2003","unstructured":"Chatfield C (2003) The analysis of time series: an introduction, vol 352, 6th edn. Chapman and Hall\/CRC, London","edition":"6"},{"key":"4995_CR20","doi-asserted-by":"publisher","first-page":"2540","DOI":"10.1016\/j.neucom.2010.06.004","volume":"73","author":"M Ardalani-Farsa","year":"2010","unstructured":"Ardalani-Farsa M, Zolfaghari S (2010) Chaotic time series prediction with residual analysis method using combined Elman\u2013NARX neural networks. Neurocomputing 73:2540\u20132553","journal-title":"Neurocomputing"},{"key":"4995_CR21","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.1007\/s00521-012-1210-0","volume":"22","author":"A Erdil","year":"2013","unstructured":"Erdil A, Arcaklioglu E (2013) The prediction of meteorological variables using artificial neural network. Neural Comput Appl 22:1677\u20131683","journal-title":"Neural Comput Appl"},{"key":"4995_CR22","doi-asserted-by":"publisher","first-page":"3335","DOI":"10.1016\/j.neucom.2008.01.030","volume":"71","author":"JM Menezes Jr","year":"2008","unstructured":"Menezes JM Jr, Barreto GA (2008) Long-term time series prediction with the NARX network: an empirical evaluation. Neurocomputing 71:3335\u20133343","journal-title":"Neurocomputing"},{"key":"4995_CR23","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.engappai.2009.04.002","volume":"22","author":"E Pisoni","year":"2009","unstructured":"Pisoni E, Farina M, Carnevale C, Piroddi L (2009) Forecasting peak air pollution levels using NARX models. Eng Appl Artif Intell 22:593\u2013602","journal-title":"Eng Appl Artif Intell"},{"key":"4995_CR24","doi-asserted-by":"publisher","first-page":"2506","DOI":"10.1016\/j.neucom.2010.06.005","volume":"73","author":"R Zemouri","year":"2010","unstructured":"Zemouri R, Gouriveau R, Zerhouni N (2010) Defining and applying prediction performance metrics on a recurrent NARX time series model. Neurocomputing 73:2506\u20132521","journal-title":"Neurocomputing"},{"key":"4995_CR25","doi-asserted-by":"publisher","unstructured":"Sergeev A, Shichkin A, Buevich A (2018) Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region. In: Proceedings of the 44th international conference on applications of mathematics in engineering and economics AMEE 2018, American Institute of Physics Inc., vol 2048, p 060005. https:\/\/doi.org\/10.1063\/1.5082120","DOI":"10.1063\/1.5082120"},{"key":"4995_CR26","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2018.01.046","volume":"286","author":"Guanghua Ren","year":"2018","unstructured":"Ren Guanghua, Cao Yuting, ShipingWen Tingwen Huang, Zeng Zhigang (2018) A modified Elman neural network with a new learning rate scheme. Neurocomputing 286:11\u201318. https:\/\/doi.org\/10.1016\/j.neucom.2018.01.046","journal-title":"Neurocomputing"},{"key":"4995_CR27","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.ecolind.2014.04.003","volume":"45","author":"F Dai","year":"2014","unstructured":"Dai F, Zhoua O, Lva Z, Wang X, Liu G (2014) Spatial prediction of soil organic matter concentration integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecol Ind 45:184\u2013194","journal-title":"Ecol Ind"},{"key":"4995_CR28","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/j.catena.2018.11.037","volume":"174","author":"AP Sergeev","year":"2019","unstructured":"Sergeev AP, Buevich AG, Baglaeva EM, Shichkin AV (2019) Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals. CATENA 174:425\u2013435","journal-title":"CATENA"},{"issue":"B","key":"4995_CR29","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.apgeochem.2017.07.007","volume":"88","author":"DA Tarasov","year":"2018","unstructured":"Tarasov DA, Buevich AG, Sergeev AP, Shichkin AV (2018) High variation topsoil pollution forecasting in the Russian Subarctic: using artificial neural networks combined with residual kriging. Appl Geochem 88(B):188\u2013197","journal-title":"Appl Geochem"},{"key":"4995_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-0953-2","volume-title":"Second-order methods for neural networks: fast and reliable training methods for multi-layer perceptrons","author":"AJ Shepherd","year":"1997","unstructured":"Shepherd AJ (1997) Second-order methods for neural networks: fast and reliable training methods for multi-layer perceptrons, vol 145. Springer, Berlin"},{"issue":"D7","key":"4995_CR31","doi-asserted-by":"publisher","first-page":"7183","DOI":"10.1029\/2000JD900719","volume":"106","author":"KE Taylor","year":"2001","unstructured":"Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106(D7):7183\u20137192","journal-title":"J Geophys Res"},{"key":"4995_CR32","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1080\/02723646.1981.10642213","volume":"2","author":"CJ Willmott","year":"1981","unstructured":"Willmott CJ (1981) On the validation of models. Phys Geogr 2:184\u2013194","journal-title":"Phys Geogr"},{"key":"4995_CR33","doi-asserted-by":"publisher","DOI":"10.1002\/joc.2419","author":"CJ Willmott","year":"2011","unstructured":"Willmott CJ, Robeson SM, Matsuura K (2011) A refined index of model performance. Int J Climatol. https:\/\/doi.org\/10.1002\/joc.2419Wiley Online Library (wileyonlinelibrary.com)","journal-title":"Int J Climatol"},{"key":"4995_CR34","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.chemolab.2014.12.008","volume":"141","author":"R Sabolova","year":"2015","unstructured":"Sabolova R, Seckarova V, Dusek J, Stehlik M (2015) Entropy based statistical inference for methane emissions released from wetland. Chemometr Intell Lab Syst 141:125\u2013133","journal-title":"Chemometr Intell Lab Syst"},{"key":"4995_CR35","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.chemolab.2013.08.006","volume":"128","author":"P Jordanova","year":"2013","unstructured":"Jordanova P, Dusek J, Stehlik M (2013) Microergodicity effects on ebullition of methane modelled by Mixed Poisson process with Pareto mixing variable. Chemometr Intell Lab Syst 128:124\u2013134","journal-title":"Chemometr Intell Lab Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04995-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-04995-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04995-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:07:44Z","timestamp":1623888464000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-04995-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,17]]},"references-count":35,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["4995"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04995-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,17]]},"assertion":[{"value":"9 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Authors declare that there is no conflict of interests regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}