AI-Driven Decision Systems for Real-Time Disaster Prediction

  • Authors

    • Alan Bundy Professor of Artificial Intelligence, University of Edinburgh, United Kingdom Author
    • Karen Sparck Jones Professor, University of Cambridge, United Kingdom Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2024PII2K9R

    Published 04-04-2024

  • Artificial Intelligence, Disaster Prediction, Machine Learning, Deep Learning, Decision Support Systems, Internet of Things, Early Warning Systems, Big Data Analytics, Smart Disaster Management, Predictive Analytics

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    A. Bundy and K. S. Jones, “AI-Driven Decision Systems for Real-Time Disaster Prediction”, ijmiet, vol. 7, no. 1, pp. 01–18, Apr. 2024, doi: 10.67228/30715628/IJMIET-2024PII2K9R.

  • Abstract

    Natural and human-induced disasters are increasing in frequency and severity due to climate change, rapid urbanization, environmental degradation, and population growth. Conventional disaster prediction methods often lack the speed and accuracy needed for real-time emergency response. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Big Data Analytics enable intelligent systems to analyze diverse real-time data from satellites, IoT sensors, weather stations, seismic networks, GIS, and social media for accurate disaster forecasting. This paper presents an AI-based decision support framework integrating data acquisition, preprocessing, feature engineering, machine learning, deep learning, and automated decision-making within a scalable cloud-edge architecture. The study also reviews existing AI-based disaster prediction approaches, identifies their limitations, and compares their performance. The findings demonstrate that AI-driven disaster prediction systems significantly improve early warning capabilities, situational awareness, resource allocation, infrastructure protection, and emergency response, ultimately reducing disaster impacts and saving lives.

  • References

    [1] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015.

    [2] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

    [3] L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001.

    [4] C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273–297, Sept. 1995.

    [5] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997.

    [6] A. Vaswani et al., "Attention Is All You Need," in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998–6008.

    [7] P. C. Shih, C. H. Hsu, and Y. T. Lin, "Artificial intelligence approaches for natural disaster prediction: A review," Sustainability, vol. 12, no. 24, pp. 1–27, 2020.

    [8] A. K. Jain and B. B. Chaudhuri, "Artificial intelligence techniques in disaster management: A review," International Journal of Disaster Risk Reduction, vol. 47, Art. no. 101539, 2020.

    [9] F. Tao, Q. Qi, A. Liu, and A. Kusiak, "Data-driven smart manufacturing," Journal of Manufacturing Systems, vol. 48, pp. 157–169, Jul. 2018.

    [10] [10] M. Chen, S. Mao, and Y. Liu, "Big data: A survey," Mobile Networks and Applications, vol. 19, no. 2, pp. 171–209, Apr. 2014.

    [11] L. Atzori, A. Iera, and G. Morabito, "The Internet of Things: A survey," Computer Networks, vol. 54, no. 15, pp. 2787–2805, Oct. 2010.

    [12] T. N. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks," in Proc. International Conference on Learning Representations (ICLR), 2017.

    [13] D. Gunning and D. Aha, "DARPA's Explainable Artificial Intelligence (XAI) Program," AI Magazine, vol. 40, no. 2, pp. 44–58, 2019.

    [14] P. Voosen, "Artificial intelligence helps predict floods and other natural disasters," Science, vol. 366, no. 6464, pp. 554–555, 2019.

    [15] United Nations Office for Disaster Risk Reduction (UNDRR), Global Assessment Report on Disaster Risk Reduction 2022. Geneva, Switzerland: UNDRR, 2022.

    [16] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

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