Data driven fuel consumption prediction model for green aviation using radial basis function neural network
Abstract In response to the growing demand for sustainable aviation, a fuel consumption prediction model based on Radial Basis Function (RBF) Neural Networks was proposed. Using high-resolution onboard Quick Access Recorder (QAR) data, which contains richer flight parameters and higher accuracy, RBF...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11941-8 |
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| _version_ | 1849766751777062912 |
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| author | Yuandi Zhao Zhongyi Wang Xiaohui Wang Ye Song Yuzhe Han |
| author_facet | Yuandi Zhao Zhongyi Wang Xiaohui Wang Ye Song Yuzhe Han |
| author_sort | Yuandi Zhao |
| collection | DOAJ |
| description | Abstract In response to the growing demand for sustainable aviation, a fuel consumption prediction model based on Radial Basis Function (RBF) Neural Networks was proposed. Using high-resolution onboard Quick Access Recorder (QAR) data, which contains richer flight parameters and higher accuracy, RBF models were constructed based on the extracted key influencing factors for different flight phases, including takeoff/climb, cruise, and descent/approach. The model provides a lightweight and computationally efficient solution for high-dimensional, nonlinear flight data, ensuring accuracy with lower computational burdens. It is suitable both for pre-flight ground-based fuel consumption prediction and deployment in resource-constrained onboard environments, enabling real-time prediction during flight operations. Experimental results showed that the RBF model’s prediction errors for the takeoff/climb, cruise, and descent/approach phases were 5.73%, 3.36%, and 14.04%, respectively, significantly outperforming the comparison models. The error variances from ten-fold cross-validation were 0.31%, 0.15%, and 0.29%, respectively, confirming the robustness of the model. Further analysis indicated that the model can be employed to evaluate the “fuel penalty for carrying additional fuel” patterns and enhance fuel efficiency. This study provided valuable insights and theoretical support for airlines in optimizing flight planning and minimizing fuel consumption, thereby contributing to the sustainable development of green aviation. |
| format | Article |
| id | doaj-art-a2d811a8f3ad48e2812a292af8ae56d8 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a2d811a8f3ad48e2812a292af8ae56d82025-08-20T03:04:29ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-11941-8Data driven fuel consumption prediction model for green aviation using radial basis function neural networkYuandi Zhao0Zhongyi Wang1Xiaohui Wang2Ye Song3Yuzhe Han4College of Air Traffic Management, Civil Aviation University of ChinaCollege of Air Traffic Management, Civil Aviation University of ChinaCollege of Computer Science and Technology, Civil Aviation University of ChinaCollege of Air Traffic Management, Civil Aviation University of ChinaCollege of Air Traffic Management, Civil Aviation University of ChinaAbstract In response to the growing demand for sustainable aviation, a fuel consumption prediction model based on Radial Basis Function (RBF) Neural Networks was proposed. Using high-resolution onboard Quick Access Recorder (QAR) data, which contains richer flight parameters and higher accuracy, RBF models were constructed based on the extracted key influencing factors for different flight phases, including takeoff/climb, cruise, and descent/approach. The model provides a lightweight and computationally efficient solution for high-dimensional, nonlinear flight data, ensuring accuracy with lower computational burdens. It is suitable both for pre-flight ground-based fuel consumption prediction and deployment in resource-constrained onboard environments, enabling real-time prediction during flight operations. Experimental results showed that the RBF model’s prediction errors for the takeoff/climb, cruise, and descent/approach phases were 5.73%, 3.36%, and 14.04%, respectively, significantly outperforming the comparison models. The error variances from ten-fold cross-validation were 0.31%, 0.15%, and 0.29%, respectively, confirming the robustness of the model. Further analysis indicated that the model can be employed to evaluate the “fuel penalty for carrying additional fuel” patterns and enhance fuel efficiency. This study provided valuable insights and theoretical support for airlines in optimizing flight planning and minimizing fuel consumption, thereby contributing to the sustainable development of green aviation.https://doi.org/10.1038/s41598-025-11941-8Green civil aviationFuel consumptionRadial basis function neural networkFuel penalty for carrying additional fuel |
| spellingShingle | Yuandi Zhao Zhongyi Wang Xiaohui Wang Ye Song Yuzhe Han Data driven fuel consumption prediction model for green aviation using radial basis function neural network Scientific Reports Green civil aviation Fuel consumption Radial basis function neural network Fuel penalty for carrying additional fuel |
| title | Data driven fuel consumption prediction model for green aviation using radial basis function neural network |
| title_full | Data driven fuel consumption prediction model for green aviation using radial basis function neural network |
| title_fullStr | Data driven fuel consumption prediction model for green aviation using radial basis function neural network |
| title_full_unstemmed | Data driven fuel consumption prediction model for green aviation using radial basis function neural network |
| title_short | Data driven fuel consumption prediction model for green aviation using radial basis function neural network |
| title_sort | data driven fuel consumption prediction model for green aviation using radial basis function neural network |
| topic | Green civil aviation Fuel consumption Radial basis function neural network Fuel penalty for carrying additional fuel |
| url | https://doi.org/10.1038/s41598-025-11941-8 |
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