Climate model selection via conformal clustering of spatial functional data Article Swipe
Veronica Villani
,
Elvira Romano
,
Jorge Mateu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10651-024-00616-8
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10651-024-00616-8
Climate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX simulation models work better for predicting average temperatures in the Campania region (Italy). The method involves two key steps: first, using functional data analysis to process climate variables and select optimal models by a hierarchical clustering procedure; second, validating the chosen models by proposing a new conformal prediction approach to the anomalies associated to each cluster.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10651-024-00616-8
- https://link.springer.com/content/pdf/10.1007/s10651-024-00616-8.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396831529
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Raw OpenAlex JSON
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https://openalex.org/W4396831529Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10651-024-00616-8Digital Object Identifier
- Title
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Climate model selection via conformal clustering of spatial functional dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-11Full publication date if available
- Authors
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Veronica Villani, Elvira Romano, Jorge MateuList of authors in order
- Landing page
-
https://doi.org/10.1007/s10651-024-00616-8Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s10651-024-00616-8.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10651-024-00616-8.pdfDirect OA link when available
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Cluster analysis, Selection (genetic algorithm), Model selection, Conformal map, Computer science, Spatial analysis, Mathematics, Data mining, Statistics, Artificial intelligence, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.approach, | 42 |
| abstract_inverted_index.conformal | 102 |
| abstract_inverted_index.proposing | 99 |
| abstract_inverted_index.research. | 13 |
| abstract_inverted_index.selection | 3 |
| abstract_inverted_index.variables | 83 |
| abstract_inverted_index.associated | 108 |
| abstract_inverted_index.behaviour, | 29 |
| abstract_inverted_index.clustering | 91 |
| abstract_inverted_index.functional | 45, 77 |
| abstract_inverted_index.predicting | 61 |
| abstract_inverted_index.prediction | 103 |
| abstract_inverted_index.procedure; | 92 |
| abstract_inverted_index.questions, | 26 |
| abstract_inverted_index.simulating | 27 |
| abstract_inverted_index.simulation | 56 |
| abstract_inverted_index.validating | 94 |
| abstract_inverted_index.appropriate | 19 |
| abstract_inverted_index.conditions. | 36 |
| abstract_inverted_index.projections | 32 |
| abstract_inverted_index.hierarchical | 90 |
| abstract_inverted_index.temperatures | 63 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.8600000143051147 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.92793464 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |