{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:05:40Z","timestamp":1777655140257,"version":"3.51.4"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"35","license":[{"start":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T00:00:00Z","timestamp":1694822400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T00:00:00Z","timestamp":1694822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s00521-023-08981-4","type":"journal-article","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T14:01:47Z","timestamp":1694872907000},"page":"24925-24946","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Parametric estimation scheme for aircraft fuel consumption using machine learning"],"prefix":"10.1007","volume":"35","author":[{"given":"Mirza Anas","family":"Wahid","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3808-8656","authenticated-orcid":false,"given":"Syed Hashim Raza","family":"Bukhari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muazzam","family":"Maqsood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farhan","family":"Aadil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Ismail","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeed Ehsan","family":"Awan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"key":"8981_CR1","unstructured":"Sultana S (2018) Economic growth of airlines industry: an overview of domestic airlines in Bangladesh. J Manag Res Anal 5"},{"issue":"4","key":"8981_CR2","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1080\/01441647.2020.1738587","volume":"40","author":"F Zhang","year":"2020","unstructured":"Zhang F, Graham DJ (2020) Air transport and economic growth: a review of the impact mechanism and causal relationships. Transp Rev 40(4):506\u2013528","journal-title":"Transp Rev"},{"key":"8981_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2019.06.007","volume":"254","author":"H Wei","year":"2019","unstructured":"Wei H, Liu W, Chen X, Yang Q, Li J, Chen H (2019) Renewable bio-jet fuel production for aviation: a review. Fuel 254:115599","journal-title":"Fuel"},{"key":"8981_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.biombioe.2020.105942","volume":"145","author":"AH Tanzil","year":"2021","unstructured":"Tanzil AH, Brandt K, Wolcott M, Zhang X, Garcia-Perez M (2021) Strategic assessment of sustainable aviation fuel production technologies: yield improvement and cost reduction opportunities. Biomass Bioenergy 145:105942","journal-title":"Biomass Bioenergy"},{"key":"8981_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2021.111680","volume":"152","author":"L Martinez-Valencia","year":"2021","unstructured":"Martinez-Valencia L, Garcia-Perez M, Wolcott MP (2021) Supply chain configuration of sustainable aviation fuel: review, challenges, and pathways for including environmental and social benefits. Renew Sustain Energy Rev 152:111680","journal-title":"Renew Sustain Energy Rev"},{"issue":"3","key":"8981_CR6","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1007\/s13272-021-00508-8","volume":"12","author":"S Baumann","year":"2021","unstructured":"Baumann S, Neidhardt T, Klingauf U (2021) Evaluation of the aircraft fuel economy using advanced statistics and machine learning. CEAS Aeronaut J 12(3):669\u2013681","journal-title":"CEAS Aeronaut J"},{"issue":"16","key":"8981_CR7","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.3390\/en12163055","volume":"12","author":"CO Trejo-Pech","year":"2019","unstructured":"Trejo-Pech CO, Larson JA, English BC, Yu TE (2019) Cost and profitability analysis of a prospective pennycress to sustainable aviation fuel supply chain in southern usa. Energies 12(16):3055","journal-title":"Energies"},{"key":"8981_CR8","doi-asserted-by":"crossref","unstructured":"Doganis R (2013) Flying off course: the economics of international airlines. Routledge","DOI":"10.4324\/9780203976197"},{"key":"8981_CR9","doi-asserted-by":"crossref","unstructured":"Badykov RR, Panshin RA, Tremkina OV, Prokofieva AA (2021) Utilization of low-potential energy through to the use in aircraft fuel system. In: IOP conference series: materials science and engineering, vol 1102(1). IOP Publishing, p 012007","DOI":"10.1088\/1757-899X\/1102\/1\/012007"},{"key":"8981_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2019.105542","volume":"96","author":"JP Eguea","year":"2020","unstructured":"Eguea JP, da Silva GPG, Catalano FM (2020) Fuel efficiency improvement on a business jet using a camber morphing winglet concept. Aerosp Sci Technol 96:105542","journal-title":"Aerosp Sci Technol"},{"key":"8981_CR11","doi-asserted-by":"crossref","unstructured":"Manna S, Biswas S, Kundu R, Rakshit S, Gupta P, Barman S (2017) A statistical approach to predict flight delay using gradient boosted decision tree. In: 2017 International conference on computational intelligence in data science (ICCIDS). IEEE, pp 1\u20135","DOI":"10.1109\/ICCIDS.2017.8272656"},{"issue":"7","key":"8981_CR12","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1016\/j.trd.2012.06.005","volume":"17","author":"H Khadilkar","year":"2012","unstructured":"Khadilkar H, Balakrishnan H (2012) Estimation of aircraft taxi fuel burn using flight data recorder archives. Transp Res Part D Transp Environ 17(7):532\u2013537","journal-title":"Transp Res Part D Transp Environ"},{"key":"8981_CR13","unstructured":"Mazareanu E (2021) Commercial airlines: worldwide fuel consumption 2005-2022. Oct. [Online]. Available: https:\/\/www.statista.com\/statistics\/655057\/fuel-consumption-of-airlines-worldwide\/"},{"key":"8981_CR14","unstructured":"Gouveia OR, Borges A, Costa D, Lopes P, Coelho D, Ferreira C, Serrano L. Development of a low-cost test bench for heavy-duty combustion engines"},{"key":"8981_CR15","doi-asserted-by":"crossref","unstructured":"Mattingly JD, Heiser WH, Pratt DT (2002) Aircraft engine design. American Institute of Aeronautics and Astronautics","DOI":"10.2514\/4.861444"},{"key":"8981_CR16","unstructured":"Dalon T. Surrogate-based optimization for optimal automatic calibration of modern automotive combustion engines at engine test bench"},{"key":"8981_CR17","unstructured":"Kondratenko O, Deyneko N, Vambol S (2015) Engine test bench as a source of danger factors in experimental researches. Ph.D. dissertation, NTU"},{"key":"8981_CR18","doi-asserted-by":"publisher","first-page":"109 246","DOI":"10.1109\/ACCESS.2019.2931767","volume":"7","author":"Y Wang","year":"2019","unstructured":"Wang Y, Shi Y, Cai M, Xu W, Pan T, Yu Q (2019) Study of fuel-controlled aircraft engine for fuel-powered unmanned aerial vehicle: energy conversion analysis and optimization. IEEE Access 7:109 246-109 258","journal-title":"IEEE Access"},{"issue":"3","key":"8981_CR19","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s40192-018-0117-8","volume":"7","author":"DM Dimiduk","year":"2018","unstructured":"Dimiduk DM, Holm EA, Niezgoda SR (2018) Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integr Mater Manuf Innov 7(3):157\u2013172","journal-title":"Integr Mater Manuf Innov"},{"key":"8981_CR20","unstructured":"Jasra S, Gauci J, Muscat A, Valentino G, Zammit-Mangion D, Camilleri R (2018) Literature review of machine learning techniques to analyse flight data"},{"issue":"3","key":"8981_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21917\/ijsc.2015.0133","volume":"5","author":"I Muhammad","year":"2015","unstructured":"Muhammad I, Yan Z (2015) Supervised machine learning approaches: a survey. ICTACT J Soft Comput 5(3):1","journal-title":"ICTACT J Soft Comput"},{"key":"8981_CR22","unstructured":"eyeofbill, F-16 jet engine test at full afterburner in the hush house. Mar 2017. [Online]. Available: https:\/\/www.youtube.com\/watch?v=Oj4w7i-TqsE"},{"issue":"11","key":"8981_CR23","doi-asserted-by":"publisher","first-page":"969","DOI":"10.2514\/3.44799","volume":"19","author":"BP Collins","year":"1982","unstructured":"Collins BP (1982) Estimation of aircraft fuel consumption. J Aircr 19(11):969\u2013975","journal-title":"J Aircr"},{"issue":"1","key":"8981_CR24","doi-asserted-by":"publisher","first-page":"67","DOI":"10.28991\/HIJ-2021-02-01-07","volume":"2","author":"H Kapeller","year":"2021","unstructured":"Kapeller H, Dvorak D, \u0160imi\u0107 D (2021) Improvement and investigation of the requirements for electric vehicles by the use of hvac modeling. HighTech Innov J 2(1):67\u201376","journal-title":"HighTech Innov J"},{"issue":"7","key":"8981_CR25","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.28991\/cej-2020-03091553","volume":"6","author":"D Qerimi","year":"2020","unstructured":"Qerimi D, Dimitrieska C, Vasilevska S, Rrecaj AA (2020) Modeling of the solar thermal energy use in urban areas. Civ Eng J 6(7):1349\u20131367","journal-title":"Civ Eng J"},{"key":"8981_CR26","unstructured":"Nuic A (2011) User manual for the base of aircraft data (bada). Eurocontrol Experimental Centre, Cedex, France, revision, vol. 3"},{"issue":"4","key":"8981_CR27","doi-asserted-by":"publisher","first-page":"384","DOI":"10.28991\/HIJ-2021-02-04-010","volume":"2","author":"MM Namar","year":"2021","unstructured":"Namar MM, Jahanian O, Shafaghat R, Nikzadfar K (2021) Engine downsizing; global approach to reduce emissions: a world-wide review. HighTech Innov J 2(4):384\u2013399","journal-title":"HighTech Innov J"},{"issue":"1","key":"8981_CR28","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s13272-019-00422-0","volume":"11","author":"S Baumann","year":"2020","unstructured":"Baumann S, Klingauf U (2020) Modeling of aircraft fuel consumption using machine learning algorithms. CEAS Aeronaut J 11(1):277\u2013287","journal-title":"CEAS Aeronaut J"},{"key":"8981_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.jairtraman.2022.102181","volume":"99","author":"C Huang","year":"2022","unstructured":"Huang C, Cheng X (2022) Estimation of aircraft fuel consumption by modeling flight data from avionics systems. J Air Transp Manag 99:102181","journal-title":"J Air Transp Manag"},{"key":"8981_CR30","doi-asserted-by":"crossref","unstructured":"Horiguchi Y, Baba Y, Kashima H, Suzuki M, Kayahara H, Maeno J (2017) Predicting fuel consumption and flight delays for low-cost airlines. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 4686\u20134693","DOI":"10.1609\/aaai.v31i2.19095"},{"key":"8981_CR31","doi-asserted-by":"crossref","unstructured":"Wang X, Chen X (2014) A support vector method for modeling civil aircraft fuel consumption with roc optimization. In: 2014 enterprise systems conference. IEEE, pp 112\u2013116","DOI":"10.1109\/ES.2014.13"},{"key":"8981_CR32","unstructured":"Chati YS, Balakrishnan H (2016) Statistical modeling of aircraft engine fuel flow rate. In: 30th congress of the international council of the aeronautical science"},{"key":"8981_CR33","unstructured":"Kang L, Hansen M (2017) Quantile regression based estimation of statistical contingency fuel. In: Twelfth USA\/Europe air traffic management research and development seminar (ATM2017)"},{"key":"8981_CR34","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.trd.2017.03.023","volume":"53","author":"C Huang","year":"2017","unstructured":"Huang C, Xu Y, Johnson ME (2017) Statistical modeling of the fuel flow rate of ga piston engine aircraft using flight operational data. Transp Res Part D Transp Environ 53:50\u201362","journal-title":"Transp Res Part D Transp Environ"},{"issue":"10","key":"8981_CR35","doi-asserted-by":"publisher","first-page":"624","DOI":"10.3390\/aerospace9100624","volume":"9","author":"J Yanto","year":"2022","unstructured":"Yanto J, Liem RP (2022) Cluster-based aircraft fuel estimation model for effective and efficient fuel budgeting on new routes. Aerospace 9(10):624","journal-title":"Aerospace"},{"issue":"2","key":"8981_CR36","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3390\/aerospace8020044","volume":"8","author":"M Uzun","year":"2021","unstructured":"Uzun M, Demirezen MU, Inalhan G (2021) Physics guided deep learning for data-driven aircraft fuel consumption modeling. Aerospace 8(2):44","journal-title":"Aerospace"},{"key":"8981_CR37","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ast.2015.11.031","volume":"49","author":"T Baklacioglu","year":"2016","unstructured":"Baklacioglu T (2016) Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks. Aerosp Sci Technol 49:52\u201362","journal-title":"Aerosp Sci Technol"},{"key":"8981_CR38","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.trd.2017.05.006","volume":"54","author":"I Pagoni","year":"2017","unstructured":"Pagoni I, Psaraki-Kalouptsidi V (2017) Calculation of aircraft fuel consumption and co2 emissions based on path profile estimation by clustering and registration. Transp Res Part D Transp Environ 54:172\u2013190","journal-title":"Transp Res Part D Transp Environ"},{"key":"8981_CR39","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1016\/j.trd.2018.09.014","volume":"65","author":"J Yanto","year":"2018","unstructured":"Yanto J, Liem RP (2018) Aircraft fuel burn performance study: a data-enhanced modeling approach. Transp Res Part D Transp Environ 65:574\u2013595","journal-title":"Transp Res Part D Transp Environ"},{"issue":"4","key":"8981_CR40","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.2514\/1.42025","volume":"46","author":"DA Senzig","year":"2009","unstructured":"Senzig DA, Fleming GG, Iovinelli RJ (2009) Modeling of terminal-area airplane fuel consumption. J Aircr 46(4):1089\u20131093","journal-title":"J Aircr"},{"key":"8981_CR41","doi-asserted-by":"crossref","unstructured":"Srivastava I, Moharir AK, Yadam G (2020) Learning interpretable rules contributing to maximal fuel rate flow consumption in an aircraft using rule based algorithms. In: 2020 IEEE international conference for innovation in technology (INOCON). IEEE, pp 1\u20138","DOI":"10.1109\/INOCON50539.2020.9298436"},{"key":"8981_CR42","unstructured":"Predict fuel flow rate of airplanes during different phases of a flight (2021) [Online]. Available: https:\/\/www.crowdanalytix.com\/contests\/predict-fuel-flow-rate-of-airplanes-during-different-phases-of-a-flight"},{"key":"8981_CR43","doi-asserted-by":"crossref","unstructured":"Li M, Zhou Q (2017) Industrial big data visualization: a case study using flight data recordings to discover the factors affecting the airplane fuel efficiency. In: 2017 IEEE Trustcom\/BigDataSE\/ICESS. IEEE, pp 853\u2013858","DOI":"10.1109\/Trustcom\/BigDataSE\/ICESS.2017.322"},{"issue":"3","key":"8981_CR44","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","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"},{"issue":"3","key":"8981_CR45","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1002\/er.3048","volume":"38","author":"JC Sousa","year":"2014","unstructured":"Sousa JC, Jorge HM, Neves LP (2014) Short-term load forecasting based on support vector regression and load profiling. Int J Energy Res 38(3):350\u2013362","journal-title":"Int J Energy Res"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08981-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08981-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08981-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T12:06:33Z","timestamp":1700136393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08981-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,16]]},"references-count":45,"journal-issue":{"issue":"35","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["8981"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08981-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,16]]},"assertion":[{"value":"19 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2023","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 have no financial or proprietary interests in any material discussed in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}