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Data-Driven Disaster Management in a Smart City

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Intelligent Transport Systems (INTSYS 2021)

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Abstract

Disasters, both natural and man-made, are extreme and complex events with consequences that translate into a loss of life and/or destruction of properties. The advances in IT and Big Data analysis represent an opportunity for the development of resilient environments once the application of analytical methods allows extracting information from a significant amount of data, optimizing the decision-making processes. This research aims to apply the CRISP-DM methodology to extract information about incidents that occurred in the city of Lisbon with emphasis on occurrences that affected buildings, constituting a tool to assist in the management of the city. Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the area most affected by occurrences. Finally, machine learning models were applied to the data and the predictive model Random Forest obtained the best result with an accuracy of 58%.

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Authors and Affiliations

  1. Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisbon, Portugal

    Sandra P. Gonçalves & Joao C Ferreira

  2. 2ISRC, Instituto Superior de Engenharia do Porto - Politécnico do Porto, Porto, Portugal

    Ana Madureira

Authors
  1. Sandra P. Gonçalves
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  2. Joao C Ferreira
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  3. Ana Madureira
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Corresponding author

Correspondence to Joao C Ferreira .

Editor information

Editors and Affiliations

  1. Lisbon University Institute, Lisbon, Portugal

    Ana Lúcia Martins

  2. Lisbon University Institute, Lisbon, Portugal

    Joao C Ferreira

  3. University of Pisa, Pisa, Italy

    Alexander Kocian

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Gonçalves, S.P., Ferreira, J.C., Madureira, A. (2022). Data-Driven Disaster Management in a Smart City. In: Martins, A.L., Ferreira, J.C., Kocian, A. (eds) Intelligent Transport Systems. INTSYS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 426. Springer, Cham. https://doi.org/10.1007/978-3-030-97603-3_9

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  • DOI: https://doi.org/10.1007/978-3-030-97603-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97602-6

  • Online ISBN: 978-3-030-97603-3

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