Leveraging Latent Dirichlet Allocation and Fuzzy Clustering for Identifying Key UAV Applications in Disaster Response

Year 2024, Volume: 1 Issue: 2, 17 - 26, 27.11.2024

Zeynep Yüksel , Nazmiye Eligüzel , Suleyman Mete

Abstract

Over the past few decades, there has been a significant increase in the occurrence of natural disasters, such as earthquakes and landslides, presenting a grave risk to the safety of people's lives and their possessions. Drones, also known as unmanned aerial systems (UAVs), are increasingly attracting the attention of organizations engaged in disaster events, especially in the context of post-disaster emergency response. This research aims to assess the use of UAV applications in the post-disaster phase through a descriptive literature analysis. The evaluation is conducted using the Latent Dirichlet Allocation (LDA) topic modelling and clustering approach, namely the fuzzy c-means algorithm. A total of 433 papers are extracted from the Scopus database. The analysis offers valuable insights into three primary domains: imaging-based damage assessment, emergency communication networks, and vehicle routing optimization. These findings emphasize the significance of technology and streamlined systems in effectively handling complex situations, such as disaster response and network management. By integrating UAVs into disaster response strategies, policymakers can significantly enhance the agility and efficiency of their operations, ultimately saving lives and minimizing the impact of natural disasters on communities. This study can assist in achieving these goals by providing valuable insights and guidance.

Keywords

Drone, Fuzzy c-means, LDA, Post-disaster, Unmanned aerial vehicle

References

• Bezdek, J. C., Ehrlich, R., and Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2–3), 191–203. https://doi.org/10.1016/0098-3004(84)90020-7
• Blei, D., Jordan, M., and Ng, A. Y. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. https://doi.org/10.1162/jmlr.2003.3.4-5.993
• Calamoneri, T., Corò, F., and Mancini, S. (2024). Management of a post-disaster emergency scenario through unmanned aerial vehicles: Multi-depot multi-trip vehicle routing with total completion time minimization. Expert Systems with Applications, 251(February), 123766. https://doi.org/10.1016/j.eswa.2024.123766
• Faiz, T. I., Vogiatzis, C., and Noor-E-Alam, M. (2024). Computational approaches for solving two-echelon vehicle and UAV routing problems for post-disaster humanitarian operations. Expert Systems with Applications, 237(PB), 121473. https://doi.org/10.1016/j.eswa.2023.121473
• Freeman, M. R., Kashani, M. M., and Vardanega, P. J. (2021). Aerial robotic technologies for civil engineering: Established and emerging practice. Journal of Unmanned Vehicle Systems, 9(2), 75–91. https://doi.org/10.1139/juvs-2020-0019
• Garnica-Peña, R. J., and Alcántara-Ayala, I. (2021). The use of UAVs for landslide disaster risk research and disaster risk management: a literature review. Journal of Mountain Science, 18(2), 482–498. https://doi.org/10.1007/s11629-020-6467-7
• Ishiwatari, M. (2024). Leveraging drones for effective disaster management: A comprehensive analysis of the 2024 Noto Peninsula earthquake case in Japan. Progress in Disaster Science, 23(July), 100348. https://doi.org/10.1016/j.pdisas.2024.100348
• Lei, J., Zhang, T., Mu, X., and Liu, Y. (2024). NOMA for STAR-RIS assisted UAV networks. IEEE Transactions on Communications, 72(3), 1732–1745. https://doi.org/10.1109/TCOMM.2023.3333880
• Liu, C., Feng, W., Chen, Y., Wang, C. X., Li, X., and Ge, N. (2020). Process-oriented optimization for beyond 5G cognitive satellite-UAV networks (Invited Paper). 2020 29th Wireless and Optical Communications Conference, WOCC 2020. https://doi.org/10.1109/WOCC48579.2020.9114919
• Lozano, J. M., and Tien, I. (2023). Data collection tools for post-disaster damage assessment of building and lifeline infrastructure systems. International Journal of Disaster Risk Reduction, 94, 103819. https://doi.org/10.1016/j.ijdrr.2023.103819
• Mao, W., and Xu, K. (2024). Enhancement of the classification performance of fuzzy c-means through uncertainty reduction with cloud model interpolation. Mathematics, 12(7), 975. https://doi.org/10.3390/math12070975
• Mohd Daud, S. M. S., Mohd Yusof, M. Y. P., Heo, C. C., Khoo, L. S., Chainchel Singh, M. K., Mahmood, M. S., and Nawawi, H. (2022). Applications of drone in disaster management: A scoping review. Science and Justice, 62(1), 30–42. https://doi.org/10.1016/j.scijus.2021.11.002
• Munawar, H. S., Ullah, F., Qayyum, S., Khan, S. I., and Mojtahedi, M. (2021). Uavs in disaster management: Application of integrated aerial imagery and convolutional neural network for flood detection. Sustainability, 13(14), 7547. https://doi.org/10.3390/su13147547
• Phadke, A., and Medrano, F. A. (2023). Examining application-specific resiliency implementations in UAV swarm scenarios. Intelligence and Robotics, 3(3), 453–478. https://doi.org/10.20517/ir.2023.27
• Qadir, Z., Le, K., Bao, V. N. Q., and Tam, V. W. Y. (2024). Deep learning-based intelligent post-bushfire detection using UAVs. IEEE Geoscience and Remote Sensing Letters, 21, 1–5. https://doi.org/10.1109/LGRS.2023.3329509
• Raivi, A. M., and Moh, S. (2024). JDACO: Joint data aggregation and computation offloading in UAV-enabled internet of things for post-disaster scenarios. IEEE Internet of Things Journal, 11(9), 16529–16544. https://doi.org/10.1109/JIOT.2024.3354950
• Wu, J., Chen, Q., Jiang, H., Wang, H., Xie, Y., Xu, W., Zhou, P., Xu, Z., Chen, L., Li, B., Wang, X., and Wu, D. O. (2024). Joint power and coverage control of massive UAVs in post-disaster emergency networks: An aggregative game-theoretic learning approach. IEEE Transactions on Network Science and Engineering, 11(4), 3782–3799. https://doi.org/10.1109/TNSE.2024.3385797
• Zhang, J., Zhu, Y., Li, X., Ming, M., Wang, W., and Wang, T. (2022). Multi-trip time-dependent vehicle routing problem with split delivery. Mathematics, 10(19), 3527. https://doi.org/10.3390/math10193527
• Zhang, R., Dou, L., Xin, B., Chen, C., Deng, F., and Chen, J. (2024). A review on the truck and drone cooperative delivery problem. Unmanned Systems, 12(5), 823-847. https://doi.org/10.1142/S2301385024300014
• Zou, R., Liu, J., Pan, H., Tang, D., and Zhou, R. (2024). An improved instance segmentation method for fast assessment of damaged buildings based on post-earthquake uav images. Sensors, 24(13), 4371. https://doi.org/10.3390/s24134371