Research on prioritization algorithm for multi-hazard warning dissemination Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.3389/feart.2025.1455528
The prompt, wide, and effective dissemination of warnings through early warning systems can allows more valuable time for disaster prevention and mitigation. However, due to the limited resources and channel bandwidth of early warning systems, the most urgent warnings may be delayed released when there are multiple pending warnings. To solve this problem, a prioritization algorithm for multi-hazard warning release is proposed. We identified several important attributes of warnings through the analysis of historical data and standardized them, resulting in six priority indicators for early warning issuance. By using entropy method to weight these indicators and combining them into a priority score, the priority of the warning is determined. We conducted retrospective tests on a list of pending warnings that occurred in Guangdong Province on 15 December 2023 and a list of pending warnings that occurred in Jiangxi Province on 23 March 2023. The results showed that this provides an effective method for managing queue systems. In the case of multiple warnings queued to be issued, it can provide an objective and quantitative queuing basis, avoiding biased conclusions drawn from artificial weighted calculation method or single attribute calculations. The algorithm is proved to be indicative when the abnormal warnings occur and improve the timeliness of emergency warning release. In a specific instance, there may be identical values for a certain attribute, resulting in the same score for the indicator determined based on that attribute, and consequently, that indicator does not play a role in the algorithm.we can omit that indicator from the formula to reduce computational load.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/feart.2025.1455528
- https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1455528/pdf
- OA Status
- gold
- References
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411809027
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411809027Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/feart.2025.1455528Digital Object Identifier
- Title
-
Research on prioritization algorithm for multi-hazard warning disseminationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-30Full publication date if available
- Authors
-
Yunyin Mo, Qiang Han, Yingying Song, Jing‐Feng Li, Jingjing ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3389/feart.2025.1455528Publisher landing page
- PDF URL
-
https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1455528/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1455528/pdfDirect OA link when available
- Concepts
-
Prioritization, Computer science, Hazard, Warning system, Algorithm, Data mining, Engineering, Telecommunications, Management science, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
4Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.warnings | 7, 38, 68, 118, 133, 161, 198 |
| abstract_inverted_index.weighted | 181 |
| abstract_inverted_index.Guangdong | 122 |
| abstract_inverted_index.algorithm | 55, 189 |
| abstract_inverted_index.attribute | 186 |
| abstract_inverted_index.bandwidth | 30 |
| abstract_inverted_index.combining | 96 |
| abstract_inverted_index.conducted | 110 |
| abstract_inverted_index.effective | 4, 150 |
| abstract_inverted_index.emergency | 205 |
| abstract_inverted_index.identical | 215 |
| abstract_inverted_index.important | 65 |
| abstract_inverted_index.indicator | 228, 237, 249 |
| abstract_inverted_index.instance, | 211 |
| abstract_inverted_index.issuance. | 86 |
| abstract_inverted_index.objective | 170 |
| abstract_inverted_index.proposed. | 61 |
| abstract_inverted_index.resources | 27 |
| abstract_inverted_index.resulting | 78, 221 |
| abstract_inverted_index.warnings. | 48 |
| abstract_inverted_index.artificial | 180 |
| abstract_inverted_index.attribute, | 220, 233 |
| abstract_inverted_index.attributes | 66 |
| abstract_inverted_index.determined | 229 |
| abstract_inverted_index.historical | 73 |
| abstract_inverted_index.identified | 63 |
| abstract_inverted_index.indicative | 194 |
| abstract_inverted_index.indicators | 82, 94 |
| abstract_inverted_index.prevention | 19 |
| abstract_inverted_index.timeliness | 203 |
| abstract_inverted_index.calculation | 182 |
| abstract_inverted_index.conclusions | 177 |
| abstract_inverted_index.determined. | 108 |
| abstract_inverted_index.mitigation. | 21 |
| abstract_inverted_index.algorithm.we | 245 |
| abstract_inverted_index.multi-hazard | 57 |
| abstract_inverted_index.quantitative | 172 |
| abstract_inverted_index.standardized | 76 |
| abstract_inverted_index.calculations. | 187 |
| abstract_inverted_index.computational | 255 |
| abstract_inverted_index.consequently, | 235 |
| abstract_inverted_index.dissemination | 5 |
| abstract_inverted_index.retrospective | 111 |
| abstract_inverted_index.prioritization | 54 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.26381225 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |