Prevention and Treatment of Natural Disasters

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Big Data Challenges and Opportunities for Disaster Early Warning System

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https://doi.org/10.54963/ptnd.v3i1.283
Poudel, N., Mani Dixit, A., Shiga, Y., Cao, Y., Zhang, Y., & Shaw, R. (2024). Big Data Challenges and Opportunities for Disaster Early Warning System. Prevention and Treatment of Natural Disasters, 3(1). https://doi.org/10.54963/ptnd.v3i1.283
Volume 3 Issue 1: June 2024
Copyright (c) 2024 Namita Poudel, Avani Mani Dixit, Yuki Shiga, Yuqiu Cao, Yanwu Zhang, Rajib Shaw
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Authors

  • Namita Poudel
    Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Japan https://orcid.org/0009-0003-0575-7002
  • Avani Mani Dixit Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Japan
  • Yuki Shiga Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Japan
  • Yuqiu Cao Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Japan
  • Yanwu Zhang Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Japan
  • Rajib Shaw Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Japan https://orcid.org/0000-0003-3153-1800

The application of big data in early warning systems (EWS) for multi-hazard risk management is becoming increasingly popular due to its enormous potential. Nevertheless, despite noteworthy achievements, there are still several enduring obstacles. This study aims to evaluate the advantages and difficulties through a comprehensive SWOT (strengths, weaknesses, opportunities, threats) analysis and the use of existing literature. An extensive literature analysis was conducted, incorporating perspectives of researchers from diverse background investigating both technical and social elements. The study results showed several notable strengths, such as unmatched accuracy and comprehensiveness, the incorporation of several data sources to improve prediction and forecasting, and the exploitation of up-to-the-minute data. On the other hand, problems, such as insufficient monitoring of hazards and a scarcity of interconnected sensors, especially common in less developed countries, were recognized. Lack of adequate coverage leaves a substantial percentage of the population vulnerable to disaster risks as a result of inadequate early warning systems. Ultimately, although big data offers significant possibilities, its complete capacity remains unexplored in the least developing nations due to existing obstacles, such as shortage of skilled worker, data accuracy and privacy which can be solved by enhancing technology education, capacity building, and practice. The objective of this study is to help such nations identify and overcome underlying challenges by implementing suitable measures.

 

Keywords:

disaster early warning system (EWS) big data SWOT

Author Biographies

Dr. Namita Poudel is a PhD in Keio University, Japan. She acted as an assistant professor at Saraswati Multiple College in Kathmandu, Nepal. Her research interests are urban-rural linkage, water systems, disaster resilience and urban resilience.
 
Dr. Avani Mani Dixit is a PhD at Keio University, Japan. He acted as a Senior Climate Change Officer at the Asian Development Bank in Nepal (2022 onwards) and a Disaster and Climate Risk Management Specialist at the United Nations and the World Bank (2008–2022). His research interests are the smart city, urban resilience, ICT for disaster risk management and disaster risk governance.
 

Dr. Yuki Shiga is a PhD at Keio University, Japan.

Yuqiu Cao is from Keio University, Japan.

Yanwu Zhang is from Keio University, Japan.
 
Prof. Dr. Rajib Shaw is a Professor at Keio University. His research interests are disaster risk governance, urban resilience, climate change adaptation and innovation in disaster risk reduction. He is a member of the Japan Society of Natural Disaster Science.
 

Highlights

  • Extensive literature analysis on Big data and Early warning system
  • SWOT analysis integrating diverse perspectives
  • Unmatched accuracy and comprehensiveness in hazard prediction
  • Improved forecasting through integration of multiple data sources
  • Identification of challenges like insufficient hazard monitoring and sensor connectivity
  • Recommendations for enhancing technology education and capacity building

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