The Role of Artificial Intelligence in Enhancing Cloud-Based Disaster Management Systems
DOI:
https://doi.org/10.35134/komtekinfo.v12i2.645Keywords:
Artificial Intelligence;, Cloud Computing;, Disaster Management;, Machine Learning;, Real-time Data Processing;, Early Warning Systems;Abstract
Disaster management systems are vital in mitigating the impacts of natural and human-induced disasters. However, traditional methods often struggle with limitations in responsiveness and efficiency, particularly as disaster events become more frequent and severe. This study investigates the role of Artificial Intelligence (AI) in enhancing cloud-based disaster management systems, focusing on improving predictive, analytical, and operational capabilities. The research examines key AI technologies that can be integrated into cloud platforms, including machine learning, natural language processing, and computer vision. AI substantially improves disaster response and recovery by enhancing real-time data processing, decision-making, and resource allocation. The study also highlights AI's potential in early warning and risk assessment, providing decision-makers with more accurate and timely information. Empirical analysis suggests that AI-enhanced cloud systems significantly reduce response times and improve resource distribution during disaster events, reducing loss of life and property. The research concludes with practical recommendations for implementing AI in cloud-based disaster management and identifying areas for future exploration. The findings underscore the transformative potential of AI in creating more resilient disaster management infrastructures.
References
Akoh Atadoga, Femi Osasona, Olukunle Oladipupo Amoo, Oluwatoyin Ajoke Farayola, Benjamin Samson Ayinla, and Temitayo Oluwaseun Abrahams, “THE ROLE OF IT IN ENHANCING SUPPLY CHAIN RESILIENCE: A GLOBAL REVIEW,” International Journal of Management & Entrepreneurship Research, vol. 6, no. 2, pp. 336–351, Feb. 2024, doi: 10.51594/ijmer.v6i2.774.
S. Gupta, S. Modgil, A. Kumar, U. Sivarajah, and Z. Irani, “Artificial intelligence and cloud-based Collaborative Platforms for Managing Disaster, extreme weather and emergency operations,” Int J Prod Econ, vol. 254, p. 108642, 2022, doi: 10.1016/j.ijpe.2022.108642.
H. S. Munawar, M. Mojtahedi, A. W. Hammad, A. Kouzani, and M. P. Mahmud, “Disruptive technologies as a solution for disaster risk management: a review,” Science of the Total Environment, vol. 806, p. 151351, 2022, doi: 10.1016/j.scitotenv.2021.151351.
S. Feng, M. Sester, and L. Zhang, “Integration of heterogeneous data for disaster prediction and management in a spatial data infrastructure,” ISPRS Int J Geoinf, vol. 8, no. 6, p. 270, 2019, doi: 10.3390/ijgi8060270.
N. Kumar and S. Sharma, “Disaster management and IoT: A bibliometric analysis,” Mater Today Proc, vol. 47, pp. 5104–5108, 2021, doi: 10.1016/j.matpr.2021.05.667.
G. Pang, “Artificial Intelligence for Natural Disaster Management,” IEEE Intell Syst, vol. 37, no. 6, pp. 3–6, Nov. 2022, doi: 10.1109/MIS.2022.3220061.
J. Yin, S. Karimi, A. Lampert, and M. Cameron, “Using social media to enhance emergency awareness,” IJCAI International Joint Conference on Artificial Intelligence, pp. 4234–4235, 2015, doi: 10.24963/ijcai.2015/591.
M. Imran, C. Castillo, F. Diaz, and S. Vieweg, “Processing social media messages in mass emergency: A survey,” ACM Computing Surveys (CSUR), vol. 47, no. 4, p. 67, 2015, doi: 10.1145/2771588.
E. Bonabeau, “Agent-based modelling: Methods and techniques for simulating human systems,” Proceedings of the National Academy of Sciences, vol. 99, no. suppl_3, pp. 7280–7287, May 2002, doi: 10.1073/pnas.082080899.
R. Shahabadkar and K. R. Shahabadkar, “Implication of Artificial Intelligence to Enhance the Security Aspects of Cloud-Enabled Internet of Things (IoT),” 2019, pp. 14–24. doi: 10.1007/978-3-030-19807-7_2.
C. Yang, Q. Huang, Z. Li, K. Liu, and F. Hu, “Big Data and cloud computing: innovation opportunities and challenges,” Int J Digit Earth, vol. 10, no. 1, pp. 13–53, Jan. 2017, doi: 10.1080/17538947.2016.1239771.
P. Raj and A. Raman, Software-Defined Cloud Centers. Cham: Springer International Publishing, 2018. doi: 10.1007/978-3-319-78637-7.
F. Ozen, “Cloud-based disaster management architecture using hybrid machine learning approach in IoT,” Multimedia Tools Appl, 2023, doi: 10.1007/s11042-023-13678-9.
D., Z. L., & N. J. F. Zhang, “A knowledge management framework for the support of decision making in humanitarian assistance/disaster relief,” Knowl Inf Syst, vol. 4, no. 4, pp. 520–520, Sep. 2002, doi: 10.1007/s101150200019.
Z. Ramadhanis and A. Akrimullah, “How effective is crowdsourced data during a crisis emergency? A case of the 2018 Palu-Donggala earthquake,” Jurnal Teknik Sipil dan Lingkungan, vol. 9, no. 2, pp. 221–230, Oct. 2024, doi: 10.29244/jsil.9.2.221-230.
M. Şengöz, “Harnessing Artificial Intelligence and Big Data for Proactive Disaster Management: Strategies, Challenges, and Future Directions,” Haliç Üniversitesi Fen Bilimleri Dergisi, vol. 7, no. 2, pp. 57–91, Oct. 2024, doi: 10.46373/hafebid.1534925.
S. Gupta, S. Modgil, A. Kumar, U. Sivarajah, and Z. Irani, “Artificial intelligence and cloud-based Collaborative Platforms for Managing Disaster, extreme weather and emergency operations,” Int J Prod Econ, vol. 254, p. 108642, Dec. 2022, doi: 10.1016/j.ijpe.2022.108642.
M. Ahmad, “The Role of Data Science and Volunteered Geographic Information in Enhancing Government Service Delivery,” 2023, pp. 254–281. doi: 10.4018/978-1-6684-9716-6.ch009.
L. Floridi, “Distributed Morality in an Information Society,” Sci Eng Ethics, vol. 19, no. 3, pp. 727–743, Sep. 2013, doi: 10.1007/s11948-012-9413-4.
M. A. Khoshkholghi, A. Abdullah, R. Latip, S. Subramaniam, and M. Othman, “Disaster Recovery in Cloud Computing: A Survey,” Computer and Information Science, vol. 7, no. 4, p. 39, Sep. 2014, doi: 10.5539/cis.v7n4p39.
R. M. Llácer-Iglesias, P. A. López-Jiménez, and M. Pérez-Sánchez, “Exploring options for energy recovery from wastewater: Evaluation of hydropower potential in a sustainability framework,” Sustain Cities Soc, vol. 95, p. 104576, Aug. 2023, doi: 10.1016/j.scs.2023.104576.
A. Sizo, A. Lino, L. P. Reis, and Á. Rocha, “An overview of assessing the quality of peer review reports of scientific articles,” Int J Inf Manage, vol. 46, pp. 286–293, Jun. 2019, doi: 10.1016/j.ijinfomgt.2018.07.002.
S. Liu and X. Xu, “Cyber-physical-social systems for command and control,” IEEE Intell Syst, vol. 33, no. 1, pp. 76–83, 2018, doi: 10.1109/MIS.2018.011681713.
X. Jiang and Z. Zeng, “Empowering multi-source SAR Flood mapping with unsupervised learning,” Environmental Research Letters, vol. 20, no. 1, p. 014006, Jan. 2025, doi: 10.1088/1748-9326/ad9491.
Z. Ma and G. Mei, “Deep learning for geological hazards analysis: Data, models, applications, and opportunities,” Earth Sci Rev, vol. 223, p. 103858, Dec. 2021, doi: 10.1016/j.earscirev.2021.103858.
A. Sutedi, H. Aulawi, E. Walujodjati, D. Destiani, and S. Fatimah, “C4.5 ALGORITHM FOR DISASTER IDENTIFIER SYSTEM,” vol. 3, no. 3, 2022, doi: 10.20884/1.jutif.2022.3.3.160.
Z. Ma and G. Mei, “Deep learning for geological hazards analysis: Data, models, applications, and opportunities,” Earth Sci Rev, vol. 223, p. 103858, Dec. 2021, doi: 10.1016/j.earscirev.2021.103858.
A. S. Albahri et al., “A systematic review of trustworthy artificial intelligence applications in natural disasters,” Computers and Electrical Engineering, vol. 118, p. 109409, Sep. 2024, doi: 10.1016/j.compeleceng.2024.109409.
V. G. Barros, J. Rapaglia, M. B. Richter, and J. F. Andrighi, “Design process in the urban context - Mobility and health in Special Flood Hazard Area,” International Journal of Disaster Risk Reduction, vol. 59, p. 102170, Jun. 2021, doi: 10.1016/j.ijdrr.2021.102170.
X. Fu et al., “Revolutionising agri‐energy: A comprehensive survey on the applications of artificial intelligence in agricultural energy internet,” Energy Internet, Nov. 2024, doi: 10.1049/ein2.12019.
K. K. Kapoor, K. Tamilmani, N. P. Rana, P. Patil, Y. K. Dwivedi, and S. Nerur, “Advances in Social Media Research: Past, Present and Future,” Information Systems Frontiers, vol. 20, no. 3, pp. 531–558, Jun. 2018, doi: 10.1007/s10796-017-9810-y.
M. F. Goodchild, “Citizens as sensors: the world of volunteered geography,” GeoJournal, vol. 69, no. 4, pp. 211–221, Nov. 2007, doi: 10.1007/s10708-007-9111-y.
M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science (1979), vol. 349, no. 6245, pp. 255–260, Jul. 2015, doi: 10.1126/science.aaa8415.
D. Shoji, R. Noguchi, S. Otsuki, and H. Hino, “Classification of Volcanic Ash Particles using a Convolutional Neural Network and Probability,” Sci Rep, vol. 8, p. 8111, 2018, doi: 10.1038/s41598-018-26445-9.
J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85–117, Jan. 2015, doi: 10.1016/j.neunet.2014.09.003.
F. Özen and A. Souri, “Cloud-based disaster management architecture using hybrid machine learning approach in IoT,” Multimed Tools Appl, vol. 83, no. 29, pp. 72357–72370, Feb. 2024, doi: 10.1007/s11042-024-18333-6.
P. Meel and D. K. Vishwakarma, “A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles,” Expert Syst Appl, vol. 177, p. 115002, Sep. 2021, doi: 10.1016/j.eswa.2021.115002.
Z. Sun, X. Cheng, R. Zhang, and B. Yang, “Factors Influencing Rumour Re-Spreading in a Public Health Crisis by the Middle-Aged and Elderly Populations,” Int J Environ Res Public Health, vol. 17, no. 18, p. 6542, Sep. 2020, doi: 10.3390/ijerph17186542.
X. Jiang and Z. Zeng, “Empowering multi-source SAR Flood mapping with unsupervised learning,” Environmental Research Letters, vol. 20, no. 1, p. 014006, Jan. 2025, doi: 10.1088/1748-9326/ad9491.
Q. Wu, Y. Chen, J. P. Wilson, X. Liu, and H. Li, “An effective parallelisation algorithm for DEM generalisation based on CUDA,” Environmental Modelling & Software, vol. 114, pp. 64–74, Apr. 2019, doi: 10.1016/j.envsoft.2019.01.002.
L. Zhou, W. Liu, C. (Victor) Shi, and G. Hua, “Platform Service Supply Chain Management: Challenges and solutions,” Int J Prod Econ, vol. 247, p. 108480, May 2022, doi: 10.1016/j.ijpe.2022.108480.
S. Pearson and A. Benameur, “Privacy, Security and Trust Issues Arising from Cloud Computing,” in 2010 IEEE Second International Conference on Cloud Computing Technology and Science, IEEE, Nov. 2010, pp. 693–702. doi: 10.1109/CloudCom.2010.66.
R. Mishra, M. Singh, M. Sharma, and S. Kumar, “Disaster Management Using Blockchain,” International Journal of Blockchains and Cryptocurrencies, vol. 4, no. 1, 2023, doi: 10.1504/IJBC.2023.10060314.
B. Meskó, G. Hetényi, and Z. Győrffy, “Will artificial intelligence solve the human resource crisis in healthcare?,” BMC Health Serv Res, vol. 18, no. 1, p. 545, Dec. 2018, doi: 10.1186/s12913-018-3359-4.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jurnal Komtekinfo

This work is licensed under a Creative Commons Attribution 4.0 International License.


