ae6b79b8-420b-464d-9b63-7903f90165d620250305065903167wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON SYSTEMS2224-26781109-277710.37394/23202http://wseas.org/wseas/cms.action?id=40671220241220242310.37394/23202.2024.23https://wseas.com/journals/systems/2024.phpJean Claude MudiluKafundaEcole Doctorale en Sciences de l’Ingenieur, Faculté Polytechnique, Université de Kinshasa, CONGOWitesyavwirwa VianneyKambaleInstitute for Smart Systems Technologies, Universität Klagenfurt, Klagenfurt, AUSTRIAKelvinEgbineInstitute for Smart Systems Technologies, Universität Klagenfurt, Klagenfurt, AUSTRIAMohamedAlsisiInstitute for Smart Systems Technologies, Universität Klagenfurt, Klagenfurt, AUSTRIAKyandoghereKyamakyaInstitute for Smart Systems Technologies, Universität Klagenfurt, Klagenfurt, AUSTRIAThis work assesses how System Dynamics (SD) complements Predictive analytics for dynamic mobile network capacity planning. Including SD within predictive analytics models improves responsiveness to the challenges posed by dynamic environments by including stocks and flows, feedback and time delays, and therefore model accuracy. Even with advances in machine learning, traditional predictive analytics still struggle with the dynamic feedback mechanisms as much. This research seeks to enhance mobile network forecasting and capacity planning using a hybrid approach employing real-time data. While computational complexity is a challenge, this integrated approach considerably improves network performance and planning accuracy.123120241231202453153655https://wseas.com/journals/systems/2024/b125102-036(2024).pdf10.37394/23202.2024.23.55https://wseas.com/journals/systems/2024/b125102-036(2024).pdf10.1007/s10639-022-11536-0N. Sghir, A. Adadi, and M. Lahmer, ”Recent advances in predictive learning analytics: A decade systematic review (2012–2022),” Education and Information Technologies, vol. 28, no. 7, pp. 8299–8333, 2023. 10.3390/electronics11040626I. Tomic, E. Bleakley, and P. Ivanis, ”Predictive capacity planning for mobile networks—ML supported prediction of network performance and user experience evolution,” Electronics, vol. 11, no. 4, p. 626, 2022. 10.1002/sdr.1646N. Darabi and N. Hosseinichimeh, ”System dynamics modeling in health and medicine: a systematic literature review,” System Dynamics Review, vol. 36, no. 1, pp. 29–73, 2020. B. K. Bala, F. M. Arshad, K. M. Noh, et al., ”System dynamics,” Modelling and Simulation, vol. 274, 2017. 10.1287/trsc.2021.1046J.-W. Jeon, O. Duru, Z. H. Munim, and N. Saeed, ”System dynamics in the predictive analytics of container freight rates,” Transportation Science, vol. 55, no. 4, pp. 946–967, 2021. 10.1109/mcom.111.2100049C. Pupiales, D. Laselva, Q. De Coninck, A. Jain, and I. Demirkol, ”Multi-connectivity in mobile networks: Challenges and benefits,” IEEE Communications Magazine, vol. 59, no. 11, pp. 116–122, 2021. 10.1007/978-3-030-16272-6_6S. Brdar, O. Novovic, N. Grujić, H. González-Vélez, C.-O. Truică, S. Benkner, E. Bajrovic, and A. Papadopoulos, ”Big data processing, analysis and applications in mobile cellular networks,” High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet, pp. 163–185, 2019. 10.3390/s21196588M. A. Kamal, H. W. Raza, M. M. Alam, M. M. Su’ud, and A. b. A. B. Sajak, ”Resource allocation schemes for 5G network: A systematic review,” Sensors, vol. 21, no. 19, p. 6588, 2021. J. A. Bastos, ”Forecasting the capacity of mobile networks,” Telecommunication Systems, vol. 72, no. 2, pp. 231–242, 2019. 10.1144/sp408.6J. Sutherland, I. Townend, Q. Harpham, and G. Pearce, ”From integration to fusion: the challenges ahead,” Special Publications, vol. 408, no. 1, pp. 35–54, 2017. 10.26599/bdma.2021.9020016A. K. Sandhu, ”Big data with cloud computing: Discussions and challenges,” Big Data Mining and Analytics, vol. 5, no. 1, pp. 32–40, 2021. F. Aslam, ”Role of cloud computing for big data,” no. September, 2023.