A novel self-organizing constructive neural network for estimating aircraft trip fuel consumption

Waqar Ahmed Khan, Sai Ho Chung, Hoi Lam Ma, Shi Qiang Liu, Ching Yuen Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)


Accurate estimation of aircraft fuel consumption is critical for airlines in terms of safety and profitability. In current practice, estimation of fuel consumption for a flight trip is usually done by engineering approaches, which mainly consider physical factors, e.g., planned weather and planned cruise level. However, the actual performance of a flight usually deviates from such estimation. Therefore, we propose a novel self-organizing constructive neural network (CNN) that features a cascade architecture and analytically determines connection weights to estimate the trip fuel of a flight. The proposed method generates non-redundant and linearly independent hidden units by an orthogonal linear transformation of operational parameters to achieve the best least-squares solution. Our findings provide insights for the aviation industry in controlling airlines’ excess fuel consumption.

Original languageEnglish
Pages (from-to)72-96
Number of pages25
JournalTransportation Research Part E: Logistics and Transportation Review
Publication statusPublished - Dec 2019


  • Aircraft fuel estimation
  • Engineering approach
  • High dimensional data
  • Machine learning
  • Neural network

ASJC Scopus subject areas

  • Business and International Management
  • Civil and Structural Engineering
  • Transportation

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