Complexity of hydrometeorological variables: false nearest neighbour algorithm Article Swipe
Namitha Saji
,
V. Jothiprakash
,
Bellie Sivakumar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2166/hydro.2024.178
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2166/hydro.2024.178
This study examines the complexity of hydrometeorological variables in the Savitri River basin in India. Specifically, it estimates the dimensionality of daily rainfall, runoff, maximum temperature, minimum temperature, pan evaporation, relative humidity, sunshine duration, and wind speed observed during 2000–2010 at two stations (Kangule and Birwadi). The false nearest neighbour (FNN) algorithm is employed to estimate the dimensionality of each variable. The dimensionality represents the number of variables dominantly governing the system. The FNN dimension values of the eight daily hydrometeorological series from each station range between 4 and 7, which may be considered as exhibiting a medium level of complexity. Among the eight series, wind speed is found to be the least complex, whereas minimum temperature and sunshine duration are the most complex. An attempt is also made to examine the effect of temporal scale on the complexity of the hydrometeorological variables, by analysing the hourly rainfall and runoff series. The results indicate that, for both rainfall and runoff, hourly data exhibits greater complexity, with two to three additional influential variables. The present results have important implications for hydrometeorological modelling, prediction, and disaster management in the small and flood-prone Savitri River basin.
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- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.2166/hydro.2024.178
- OA Status
- diamond
- References
- 58
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4401395864Canonical identifier for this work in OpenAlex
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https://doi.org/10.2166/hydro.2024.178Digital Object Identifier
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Complexity of hydrometeorological variables: false nearest neighbour algorithmWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-08-01Full publication date if available
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Namitha Saji, V. Jothiprakash, Bellie SivakumarList of authors in order
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https://doi.org/10.2166/hydro.2024.178Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.2166/hydro.2024.178Direct OA link when available
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Hydrometeorology, Algorithm, Computer science, Mathematics, Pattern recognition (psychology), Artificial intelligence, Geography, Meteorology, PrecipitationTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.considered | 94 |
| abstract_inverted_index.dominantly | 69 |
| abstract_inverted_index.exhibiting | 96 |
| abstract_inverted_index.management | 185 |
| abstract_inverted_index.modelling, | 181 |
| abstract_inverted_index.represents | 64 |
| abstract_inverted_index.variables, | 143 |
| abstract_inverted_index.variables. | 172 |
| abstract_inverted_index.2000–2010 | 40 |
| abstract_inverted_index.complexity, | 165 |
| abstract_inverted_index.complexity. | 101 |
| abstract_inverted_index.flood-prone | 190 |
| abstract_inverted_index.influential | 171 |
| abstract_inverted_index.prediction, | 182 |
| abstract_inverted_index.temperature | 117 |
| abstract_inverted_index.evaporation, | 30 |
| abstract_inverted_index.implications | 178 |
| abstract_inverted_index.temperature, | 26, 28 |
| abstract_inverted_index.Specifically, | 16 |
| abstract_inverted_index.dimensionality | 20, 58, 63 |
| abstract_inverted_index.hydrometeorological | 7, 81, 142, 180 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.16998288 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |