A systematic review of existing early warning Systems' challenges and opportunities in cloud computing early warning systems
Open accessAbstract
This paper assessed existing EWS challenges and opportunities in cloud computing through the PSALSAR framework for systematic literature review and meta-analysis. The research used extant literature from Scopus and Web of Science, where a total of 2516 pieces of literature were extracted between 2004 and 2022, and through inclusion and exclusion criteria, the total was reduced to 98 for this systematic review. This review highlights the challenges and opportunities in transferring in-house early warning systems (that is, non-cloud) to the cloud computing infrastructure. The different techniques or approaches used in different kinds of EWSs to facilitate climate-related data processing and analytics were also highlighted. The findings indicate that very few EWSs (for example, flood, drought, etc.) utilize the cloud computing infrastructure. Many EWSs are not leveraging the capability of cloud computing but instead using online application systems that are not cloud-based. Secondly, a few EWSs have harnessed the computational techniques and tools available on a single platform for data processing. Thirdly, EWSs combine more than one fundamental tenet of the EWS framework to provide a holistic warning system. The findings suggest that reaching a global usage of climate-related EWS may be challenged if EWSs are not redesigned to fit the cloud computing service infrastructure.
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Network and connectivity limitations apply to the practice of acquiring data in real-time systems such as algal cultivation for biomass production, low transmission rates to cloud servers, limited transmission reliability, and network capacity to boost data volume.
“This applies to the practice of acquiring data in real-time systems [51] such as ACB, low transmission rates to cloud servers [49], limited transmission reliability [52], and network capacity to boost data volume.”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [49]
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References 104 · Cited by 48
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- World Meteorological Organization. WMO Bulletin: Early Warning and Anticipatory Action. Available online: https://public. wmo.int/en/media/news/wmo-bulletin-early-warning-and-anticipatory-action (accessed on 5 April 2023).
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- Booth, A.; Sutton, A.; Papaioannou, D. Systematic Approaches to a Successful Literature Review; Sage: Hamilton, ON, Canada, 2016.
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- Authors
- Agbehadji, I. E., Mabhaudhi, T., Botai, J., & Masinde, M.
- Affiliations
- University of KwaZulu-Natal (ZA)
- International Water Management Institute (ZA)
- South African Weather Service (ZA)
- Central University of Technology (ZA)
- Corresponding author
- Agbehadji, Israel Edem
- Venue
- Climate
- Publisher
- MDPI AG
- Published
- 2023-09-08
- Volume
- 11
- Issue
- 9
- Pages
- 188
- ISSN
- 2225-1154 (ISSN-L)
- OpenAlex
- W4386615262
- Cited by
- 48
- References
- 104
- Citations by year
- 2026: 17 · 2025: 18 · 2024: 12 · 2023: 1
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Climate (publishedVersion, cc-by)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion, other-oa)
- CGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research) (submittedVersion, cc-by)
- Funders
- Government of Flanders
- Topics
- Physical Sciences › Earth and Planetary Sciences › Geology › Environmental Monitoring and Data Management
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Social Sciences › Sociology and Political Science › Disaster Management and Resilience
- Keywords
- early warning systems, cloud computing, cloud migration, flood early warning, drought early warning, climate data processing
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