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Mini UAVS’ flight data estimation in navigation phase with LSTM method within the CRISP-DM framework

Published online by Cambridge University Press:  06 March 2026

M. Konar Open the ORCID record for M. Konar [Opens in a new window] ,
D. Özdemir Open the ORCID record for D. Özdemir [Opens in a new window] ,
M. Fenerci Open the ORCID record for M. Fenerci [Opens in a new window]  and
M. Erşen Open the ORCID record for M. Erşen [Opens in a new window]
M. Konar*
Affiliation:
Faculty of Aeronautics and Astronautics, Aeronautical Electrical and Electronics , Erciyes University, Kayseri, Türkiye
D. Özdemir
Affiliation:
Faculty of Aeronautics and Astronautics, Aeronautical Electrical and Electronics , Erciyes University, Kayseri, Türkiye Graduate School of Natural and Applied Science, Aeronautical Electrical and Electronics, Erciyes University, Kayseri, Türkiye
M. Fenerci
Affiliation:
Graduate School of Natural and Applied Science, Aeronautical Electrical and Electronics, Erciyes University, Kayseri, Türkiye Faculty of Computer and Information Technologies, Information Security Technology, Cappadocia University, Nevsehir, Türkiye
M. Erşen
Affiliation:
Graduate School of Natural and Applied Science, Aeronautical Electrical and Electronics, Erciyes University, Kayseri, Türkiye Vocational School of Information Technologies, Unmanned Vehicle Department, Kayseri University, Kayseri, Türkiye
*
Corresponding author: M. Konar; Email: mkonar@erciyes.edu.tr
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Abstract

The privatisation of unmanned aerial vehicles (UAVS) used for situational awareness purposes to ensure their own situational awareness based on parameters gives direction to progress and provides a basic framework for future studies. In this context, a unique communication system architecture was proposed for obtaining state-of-the-art mini-UAV data and evaluations were carried out on the basis of data flow and parameters. Within the scope of the evaluation this study postulates a trailblazing approach as a means of optimising flight data pattern recognition by integrating Cross Industry Standard Process for Data Mining (CRISP-DM) and long short-term memory (LSTM)-based predictive depiction. By leveraging the structured framework of CRISP-DM and the sequential learning capabilities of LSTM, this research aims to enhance the accuracy of mini unmanned aerial vehicle systems (UAVS) flight scenario predictive reliability. This framework is applied to navigation phase, where the overall flight trajectory can be seen, and accurate forecasting and pattern recognition are critical for optimising operational efficiency. The findings have displayed the ability to perform high-accuracy predictions of flight parameters within a structured process. The experimental results demonstrate that key flight parameters can be predicted with near–comma-level numerical accuracy, indicating a high level of estimation precision. Through facilitating immediate data transfer and organised navigation phase evaluation, this research provides a methodical strategy for managing flight data, simultaneously contributing notably to UAV decision support systems and self-qualification engineering.

Keywords

autonomous flight predictionflight data estimationmini UAVSnavigation phase

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 130 , Issue 1347 , May 2026 , pp. 1546 - 1565
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Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Azevedo, A. and Santos, M.F. KDD, SEMMA and CRISP-DM: A Parallel Overview, IADS-DM, 2008, p 152.Google Scholar
Barabash, O.V., Dakhno, N. and Shevchenko, H. “Modified gradient method in a decision support system for control unmanned aerial vehicles,” science and education a new dimension, Nat. Tech. Sci., 2017, 16, pp 60–62.Google Scholar
Borst, N. and Verhagen, W.J.C. Introducing CNN-LSTM network adaptations to improve remaining useful life prediction of complex systems, Aeronaut. J., 2023, 127, (1318), pp 2143–2153.CrossRefGoogle Scholar
Cazacu, M. and Titan, E. Adapting CRISP-DM for social sciences, BRAIN: Broad Research in Artificial Intelligence and Neuroscience, 2021, 11, Suppl. 2, pp 99–106.CrossRefGoogle Scholar
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. The CRISP-DM User Guide, 4th CRISP-DM SIG Workshop, Brussels, Mar. 1999.Google Scholar
Duan, Y., Lv, Y. and Wang, F.Y. Travel time prediction with LSTM neural network, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), IEEE, Nov. 2016, pp 1053–1058.CrossRefGoogle Scholar
Dutta, A.K., Albagory, Y., Sait, A.R.W. and Keshta, I.M. Autonomous unmanned aerial vehicles based decision support system for weed management, Comput. Mater. Contin., 2022, 73, (1), pp 899–915.Google Scholar
Gao, R., Huo, Y., Bao, S., Tang, Y., Antic, S.L. and Epstein, E.S. Distanced LSTM: time-distanced gates in long short-term memory models for lung cancer detection, in D. Shen, T. Liu, Y. Shi, and X. Xu (Eds.), Machine Learning in Medical Imaging, MLMI 2019, Lecture Notes in Computer Science, Vol. 11861, Springer, 2019, pp 310–318.Google Scholar
Gers, F.A., Schmidhuber, J. and Cummins, F. Continual prediction using LSTM with forget gates, in B. Apolloni, M. Marinaro, and R. Tagliaferri (Eds.), Neural Nets WIRN Vietri-99, Springer, 1999, pp 133–138.CrossRefGoogle Scholar
Gopali, S., Abri, F., Siami-Namini, S. and Namin, A.S. December. A comparison of TCN and LSTM models in detecting anomalies in time series data. 2021 IEEE International Conference on Big Data (Big Data), IEEE, 2021, pp 2415–2420.Google Scholar
Graves, A. Long short-term memory, in Supervised Sequence Labelling with Recurrent Neural Networks, Springer, 2012, pp 37–45.CrossRefGoogle Scholar
Ho, D.T., Grøtli, E.I., Sujit, P.B., Johansen, T.A. and Sousa, J.B., Optimization of wireless sensor network and UAV data acquisition, J. Intell. Rob. Syst., 2015, 78, pp 159–179.CrossRefGoogle Scholar
Hochreiter, S. and Schmidhuber, J. Long short-term memory, Neural. Comput., 1997, 9, (8), pp 1735–1780.CrossRefGoogle ScholarPubMed
Hu, J., Wang, X., Zhang, Y., Zhang, D., Zhang, M. and Xue, J. Time series prediction method based on variant LSTM recurrent neural network, Neural. Process. Lett., 2020, 52, pp 1485–1500.CrossRefGoogle Scholar
IBM, IBM SPSS Modeler CRISP-DM Guide, IBM Corp., 2002. [Online] Available: https://www.ibm.com/docs/it/SS3RA7_18.3.0/pdf/ModelerCRISPDM.pdf [accessed Jul. 2024].Google Scholar
Isbilen, F., Bektas, O. and Konar, M. Deep learning and similarity-based models for predicting turbofan engine remaining useful life: insights from the CMAPSS dataset, Aeronaut. J., 2025, 129, (1337), pp 2004–2035.CrossRefGoogle Scholar
Keneni, B.M. Evolving Rule Based Explainable Artificial Intelligence for Decision Support System of Unmanned Aerial Vehicles, M.S. Thesis, University of Toledo, OH, 2018.CrossRefGoogle Scholar
Khalil, K., Eldash, O., Kumar, A. and Bayoumi, M. Economic LSTM approach for recurrent neural networks, IEEE Trans. Circuits. Syst. II Express. Briefs., 2019, 66, (11), pp 1885–1889.Google Scholar
Konar, M., Özdemir, D., Erşen, M. and Fenerci, M. Acquisition of flight data from mini unmanned aerial systems, J. Aviat., 2024, 8, (3), pp 221–228.CrossRefGoogle Scholar
LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. Gradient-based learning applied to document recognition, Proc. IEEE, 1998, 86, (11), pp 2278–2324.CrossRefGoogle Scholar
Li, D. Preprocessing and analysis method of unplanned event data for flight attendants based on CNN-GRU, Int. J. Adv. Comput. Sci. Appl., 2024, 15, (11), pp 899--915.Google Scholar
Luan, Y. and Lin, S. Research on text classification based on CNN and LSTM, 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), IEEE, 2019, pp 352–355.CrossRefGoogle Scholar
Mariscal, G., Marban, O. and Fernandez, C. A survey of data mining and knowledge discovery process models and methodologies, Knowl. Eng. Rev., 2010, 25, (2), pp 137–166.CrossRefGoogle Scholar
Matignon, R. Data Mining Using SAS Enterprise Miner, John Wiley & Sons, 2007, New York.CrossRefGoogle Scholar
Nadali, A., Kakhky, E.N. and Nosratabadi, H.E. Evaluating the success level of data mining projects based on CRISP-DM methodology by a fuzzy expert system, 2011 3rd International Conference on Electronics Computer Technology, IEEE, Apr. 2011, 6, pp 161–165.Google Scholar
Nanduri, A. and Sherry, L. Anomaly detection in aircraft data using recurrent neural networks (RNN), 2016 Integrated Communications Navigation and Surveillance (ICNS), IEEE, Apr. 2016, pp 5C2–1.CrossRefGoogle Scholar
Nanyonga, A., Wasswa, H. and Wild, G. Phase of flight classification in aviation safety using LSTM, GRU, and BiLSTM: a case study with ASN dataset, 2023 International Conference on High Performance Big Data and Intelligent Systems (HDIS), IEEE, Dec. 2023, pp 24–28.CrossRefGoogle Scholar
NCR, P.C., Clinton, J., NCR, R.K., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R., CRISP-DM 1.0, NCR Corp., 1999.Google Scholar
Özdemir, D. Acquisition of flight data of mini unmanned aerial vehicles (Master’s thesis, Erciyes University, Institute of Science, Kayseri, Turkey), 2024.Google Scholar
Plotnikova, V., Dumas, M. and Milani, F. Adapting the CRISP-DM data mining process: a case study in the financial services domain, International Conference on Research Challenges in Information Science, Springer, May 2021, pp 55–71.CrossRefGoogle Scholar
Porat, T., Oron-Gilad, T., Silbiger, J. and Rottem-Hovev, M. ‘Castling Rays’: A Decision Support Tool for UAV-Switching Tasks, CHI’10 Extended Abstracts on Human Factors in Computing Systems, 2010, pp 3589–3594.CrossRefGoogle Scholar
Pulver, A. and Lyu, S. LSTM with working memory, 2017 International Joint Conference on Neural Networks (IJCNN), IEEE, May 2017, pp 845–851.CrossRefGoogle Scholar
Saltz, J.S. CRISP-DM for data science: strengths, weaknesses and potential next steps, 2021 IEEE International Conference on Big Data (Big Data), IEEE, Dec. 2021, pp 2337–2344.CrossRefGoogle Scholar
Schäfer, F., Zeiselmair, C., Becker, J. and Otten, H. Synthesizing CRISP-DM and quality management: a data mining approach for production processes, 2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), IEEE, Nov. 2018, pp 190–195.CrossRefGoogle Scholar
Schröer, C., Kruse, F. and Gómez, J.M. A systematic literature review on applying CRISP-DM process model, Procedia. Comput. Sci., 2021, 181, pp 526–534.CrossRefGoogle Scholar
Shafique, U. and Qaiser, H. A comparative study of data mining process models (KDD, CRISP-DM and SEMMA), Int. J. Innov. Scientific. Res., 2014, 12, (1), pp 217–222.Google Scholar
Shearer, C. First CRISP-DM 2.0 Workshop Held, [Online], Available: https://www.kdnuggets.com/news/2006/n19/4i.html [accessed Aug. 2024].Google Scholar
Shi, Z., Xu, M., Pan, Q., Yan, B. and Zhang, H. LSTM-based flight trajectory prediction, 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, Jul. 2018, pp 1–8.CrossRefGoogle Scholar
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 2014, 15, (1), pp 1929–1958.Google Scholar
Stamate, M.A., Pupăză, C., Nicolescu, F.A. and Moldoveanu, C.E. Improvement of hexacopter UAVs attitude parameters employing control and decision support systems, Sensors, 2023, 23, (3), p 1446.CrossRefGoogle ScholarPubMed
Tuli, S., Casale, G. and Jennings, N.R. Tranad: deep transformer networks for anomaly detection in multivariate time series data. arXiv preprint arXiv:2201.07284, 2022.Google Scholar
Wenzel, K.E., Masselli, A. and Zell, A. Automatic take off, tracking and landing of a miniature UAV on a moving carrier vehicle, J. Intell. Rob. Syst., 2011, 61, pp 221–238.CrossRefGoogle Scholar
Wu, T., Lin, Z., Huang, J., Ding, Y., Maihemuti, A. and Xu, X. Flight quality assessment in full flight phase based on KOA-CNN-GRU-self-attention, Bull. Pol. Acad. Sci. Tech. Sci., 2024, 72, (6), e151677.Google Scholar
Xiao, K., Zhao, J., He, Y., Li, C. and Cheng, W. Abnormal behavior detection scheme of UAV using recurrent neural networks, IEEE Access., 2019, 7, pp 110293–110305.CrossRefGoogle Scholar
Yadav, A., Jha, C.K. and Sharan, A. Optimizing LSTM for time series prediction in Indian stock market, Procedia. Comput. Sci., 2020, 167, pp 2091–2100.CrossRefGoogle Scholar
Yamak, P.T., Yujian, L. and Gadosey, P.K. December. A comparison between ARIMA, LSTM, and gru for time series forecasting, Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, 2019, pp 49–55.CrossRefGoogle Scholar
Yang, L., Li, S., Zhang, Y., Zhu, C. and Liao, Z. Deep learning-assisted unmanned aerial vehicle flight data anomaly detection: a review, IEEE Sens. J., 2024, 24, (20), pp 31681--31695.Google Scholar
Zhang, J. and Man, K.F. October. Time series prediction using RNN in multidimension embedding phase space, SMC’98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. no. 98CH36218), IEEE, 1998, 2, pp 1868–1873.Google Scholar
Zhang, J. and Zhang, P. Time Series Analysis Methods and Applications for Flight Data, Springer, 2017, Berlin/Heidelberg, Germany.CrossRefGoogle Scholar