Review on Real-Time Rate of Climb Prediction System

Abstract

Real-time estimation of the rate of climb (RoC) is a vital parameter in modern aviation, essential for ensuring optimal flight performance, safety, and energy management. This paper presents a comprehensive review of the model\'s architecture, the training process, evaluation metrics, and results obtained through simulation. The performance of the model, demonstrated through metrics like R² score and RMSE, indicates the effectiveness of neural networks in accurately capturing the nonlinear dynamics of flight.

Introduction

The rate of climb (RoC) is a crucial measure of aircraft vertical speed during ascent, important for air traffic control, safety, and fuel efficiency. Traditional physics-based models for RoC calculation often fail to capture complex, nonlinear flight dynamics. To address this, machine learning—particularly neural networks—has been adopted to predict RoC in real time using operational flight data.

This project involved developing a neural network-based RoC prediction system during an engineering internship, using Python and libraries like TensorFlow and Keras. The model uses features such as thrust, drag, weight, velocity, and climb angle to infer RoC from historical flight data, achieving accurate and generalizable predictions.

The literature review highlights various related studies that applied machine learning and simulation approaches to flight performance prediction. These works demonstrate the benefits of machine learning over classical models, the importance of robust data preprocessing, feature selection, uncertainty handling, and real-time applicability. The review also underscores the value of incorporating aircraft-specific inputs and validates the use of neural networks for this task.

Conclusion

The real-time rate of climb prediction system developed using a neural network model has demonstrated the practical potential of applying machine learning techniques to aviation performance analysis. By utilizing key flight parameters and implementing a structured neural network in Python, the system effectively models the complex relationships involved in aircraft climb behaviour. The project highlights the feasibility of integrating such predictive systems into flight planning and monitoring frameworks. Looking ahead, the model can be enhanced further by incorporating additional parameters such as environmental conditions, altitude layers, and engine-specific data to improve accuracy and adaptability. There is also scope for deploying the model in real-time embedded systems within aircraft for onboard decision support, alert generation, and performance optimization. This work lays a strong foundation for continued exploration of intelligent flight analytics and their applications in modern avionics.

References

[1] K. Sharma and P. Banerjee, “Development of a Machine Learning Model for Predicting Abnormalities of Commercial Airplanes,” Data Science and Management, vol. 7, pp. 256–265, 2024. [Online]. Available: ScienceDirect. [2] L. Zhang and M. Evans, “Aviation Safety and Accident Survivability: Where is the Need for Aviation Rescue Fire Fighting Services Greatest?” Safety Science, Elsevier, 2024. [3] T. Brooks and F. Harper, “A Probabilistic Model for Aircraft in Climb Using Monotonic Functional Gaussian Process Emulators,” Proceedings of the Royal Society A, 2023. [Online]. Available: Royal Society Publishing. [4] S. Patel and H. Yamada, “Improving Climb Performance Prediction in Air Traffic Control with Machine Learning and Full Flight Simulator Verification,” in Proc. 38th Digital Avionics Systems Conf. (DASC), IEEE/AIAA, 2019. [Online]. Available: IEEE Xplore. [5] F. Dubois and A. Sanchez, “Machine Learning Applied to Airspeed Prediction during Climb,” in 11th USA/EUROPE ATM R&D Seminar, Lisbon, Portugal, June 2022. [Online]. Available: HAL Open Science. [6] R. Karthik and S. Anand, “Modeling of Commercial Aircraft Elevation Climb and Descent,” International Journal of Engineering and Information Systems (IJEAIS), 2020. [7] M. Ruiz and C. Bernard, “Ground-Based Prediction of Aircraft Climb: Regression Methods vs. Point-Mass Model,” in Proc. Complex World 2019, 1st Annual Complex World Conf., Seville, Spain, July 2019. [Online]. Available: HAL Open Science. [8] Y. Chen and D. Fischer, “Learning Aircraft Operational Factors to Improve Aircraft Climb Prediction,” Transportation Research Part C, Elsevier, 2018. [9] P. Thompson and G. Lee, “Advanced Aircraft Performance Analysis,” Aircraft Engineering and Aerospace Technology, Emerald Insight Publishing, 2018. [10] M. Verma and H. Liu, “Assessing the Impact of Relaxing Cruise Operations with a Reduction of the Minimum RoC and Step Climb Heights,” Journal of Air Transport Management, Elsevier, 2017. [11] A. Ivanov and S. Park, “Machine Learning and Mass Estimation Methods for Ground-Based Aircraft Climb Prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 6, pp. 3581–3592, Dec. 2015. [Online]. Available: IEEE Xplore. [12] Z. Yang, R. Tang, J. Bao, J. Lu, and Z. Zhang, “A Real-Time Trajectory Prediction Method of Small-Scale Quadrotors Based on GPS Data and Neural Network,” Sensors, vol. 20, no. 24, p. 7061, 2020. [13] R. Alligier, D. Gianazza, and N. Durand, “Machine Learning Applied to Airspeed Prediction During Climb,” in 11th USA/Europe ATM R&D Seminar, Lisbon, Portugal, Jun. 2015. [14] M. Poppe, R. Scharff, J. Buxbaum, and D. Fieberg, “Flight Level Prediction with a Deep Feedforward Network,” in Proceedings of the SESAR Innovation Days, 2018. [15] X. He, F. He, X. Zhu, and L. Li, “Data-driven Method for Estimating Aircraft Mass from Quick Access Recorder using Aircraft Dynamics and Multilayer Perceptron Neural Network,” arXiv preprint arXiv:2012.05907, 2020. [16] N. Pepper and M. Thomas, “Learning Generative Models for Climbing Aircraft from Radar Data,” Journal of Guidance, Control, and Dynamics, vol. 46, no. 4, pp. 789–799, 2023. [17] A. Hadjaz, G. Marceau, P. Savéant, and M. Schoenauer, “Online Learning for Ground Trajectory Prediction,” arXiv preprint arXiv:1212.3998, 2012. [18] M. Verma and H. Liu, “Assessing the Impact of Relaxing Cruise Operations with a Reduction of the Minimum RoC and Step Climb Heights,” Journal of Air Transport Management, vol. 65, pp. 1–9, 2017. [19] Y. Chen and D. Fischer, “Learning Aircraft Operational Factors to Improve Aircraft Climb Prediction,” Transportation Research Part C: Emerging Technologies, vol. 95, pp. 1–14, 2018. [20] A. Ivanov and S. Park, “Machine Learning and Mass Estimation Methods for Ground-Based Aircraft Climb Prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 6, pp. 3581–3592, Dec. 2015.

Copyright

Copyright © 2025 Sanketh T S, Trupti Shripad Tagare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.