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

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.