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Multidimensional scaling and visualization of patterns in global large-scale accidents

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  • Lopes, António M.
  • Machado, J.A. Tenreiro

Abstract

Catastrophic events have been commonly referred to as phase transitions in complex systems (CS). This paper proposes an approach based on unsupervised machine learning to identify phases and phase transitions in the dynamics of CS. The testbed is a dataset of causalities and events associated with global large-scale accidents. Multidimensional time-series are generated from the raw data and are interpreted as the output of a CS. The time-series are normalized and segmented in the time-domain, and the resulting objects are used to characterize the behavior of the dynamical process. The objects are compared through a number of distances and the information by the multidimensional scaling (MDS) technique, respectively. The time is displayed as a parametric variable. The generated portraits have a complex nature, with periods of chaotic-like behavior, and are analyzed in terms of the emerging patterns. The results show that the adoption of MDS is a relevant modeling tool using present day computational resources.

Suggested Citation

  • Lopes, António M. & Machado, J.A. Tenreiro, 2022. "Multidimensional scaling and visualization of patterns in global large-scale accidents," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:chsofr:v:157:y:2022:i:c:s0960077922001618
    DOI: 10.1016/j.chaos.2022.111951
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    References listed on IDEAS

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    1. Alexander F. Siegenfeld & Yaneer Bar-Yam, 2020. "An Introduction to Complex Systems Science and Its Applications," Complexity, Hindawi, vol. 2020, pages 1-16, July.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    3. Kaveh, Hojjat & Salarieh, Hassan, 2020. "A new approach to extreme event prediction and mitigation via Markov-model-based chaos control," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    4. José A. Tenreiro Machado & António M. Lopes & Maria Eugénia Mata, 2020. "Computer Analysis of Human Belligerency," Mathematics, MDPI, vol. 8(8), pages 1-24, July.
    5. António M Lopes & JA Tenreiro Machado, 2015. "Dynamical Analysis and Visualization of Tornadoes Time Series," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-20, March.
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    Cited by:

    1. Wang, Li-Na & Huang, Yu-Wen & Zang, Chen-Rui & Cao, Jia-Qi & Meng, Yao, 2025. "Optimized event synchronization method: Identifying synchronous spatiotemporal patterns of extreme events," Chaos, Solitons & Fractals, Elsevier, vol. 198(C).

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