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Dynamic modeling of an early warning system for natural disasters

Author

Listed:
  • Glayse Ferreira Perroni da Silva
  • Ana Lúcia Pegetti
  • Maria Teresa Piacesi
  • Mischel Carmen Neyra Belderrain
  • Níssia Carvalho Rosa Bergiante

Abstract

Natural disasters have many consequences in terms of human lives, material, economic, and/or environmental damages. Among preventive and mitigation measures, it is recognized that early warning systems (EWS) are an effective and essential tool to minimize damages caused by natural disasters. Using the experience of CEMADEN (National Early Warning and Monitoring Centre of Natural Disasters) in Brazil, this paper aims to investigate, through a systems approach, what factors may interfere with the effectiveness of EWSs. A case study was developed based on interviews with experts from CEMADEN. Those interviews generated cognitive maps that translated the perceptions of the experts and were used to structure the problem and to support the construction of a systemic model. The model allowed the analysis of the EWS, identifying behaviors, as reinforcement and balancing loops, not always intuitive, to support better management and planning decisions to improve the system effectiveness.

Suggested Citation

  • Glayse Ferreira Perroni da Silva & Ana Lúcia Pegetti & Maria Teresa Piacesi & Mischel Carmen Neyra Belderrain & Níssia Carvalho Rosa Bergiante, 2020. "Dynamic modeling of an early warning system for natural disasters," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(2), pages 292-314, March.
    • Handle: RePEc:bla:srbeha:v:37:y:2020:i:2:p:292-314
    DOI: 10.1002/sres.2628
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    Cited by:

    1. Alireza Moumivand & Adel Azar & Abbas Toloie Eshlaghy, 2022. "Combined soft system methodology and agent‐based simulation for multi‐methodological modelling," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(2), pages 200-217, March.

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