Using self–organizing maps and machine learning models to assess mollusc community structure in relation to physicochemical variables in a West Africa river–estuary system
• Mollusc community ecology analysis using SOM and machine learning models. • SOM in conjunction with IndVal, LDA and RF models are effective tools. • Mollusc assemblages are structured by physicochemical variables related to the river–estuary continuum. • Progressively downstream ward habitats face...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Koudenoukpo, Zinsou Cosme [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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Enthalten in: The capacity for acute exercise to modulate emotional memories: A review of findings and mechanisms - Keyan, Dharani ELSEVIER, 2019, integrating monitoring, assessment and management, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:126 ; year:2021 ; pages:0 |
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| DOI / URN: |
10.1016/j.ecolind.2021.107706 |
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Koudenoukpo, Zinsou Cosme |
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10.1016/j.ecolind.2021.107706 |
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150 610 |
| title_sort |
using self–organizing maps and machine learning models to assess mollusc community structure in relation to physicochemical variables in a west africa river–estuary system |
| title_auth |
Using self–organizing maps and machine learning models to assess mollusc community structure in relation to physicochemical variables in a West Africa river–estuary system |
| abstract |
• Mollusc community ecology analysis using SOM and machine learning models. • SOM in conjunction with IndVal, LDA and RF models are effective tools. • Mollusc assemblages are structured by physicochemical variables related to the river–estuary continuum. • Progressively downstream ward habitats faced to increasing anthropogenic stress. • Our approach can likely be applied for a variety of freshwater systems. |
| abstractGer |
• Mollusc community ecology analysis using SOM and machine learning models. • SOM in conjunction with IndVal, LDA and RF models are effective tools. • Mollusc assemblages are structured by physicochemical variables related to the river–estuary continuum. • Progressively downstream ward habitats faced to increasing anthropogenic stress. • Our approach can likely be applied for a variety of freshwater systems. |
| abstract_unstemmed |
• Mollusc community ecology analysis using SOM and machine learning models. • SOM in conjunction with IndVal, LDA and RF models are effective tools. • Mollusc assemblages are structured by physicochemical variables related to the river–estuary continuum. • Progressively downstream ward habitats faced to increasing anthropogenic stress. • Our approach can likely be applied for a variety of freshwater systems. |
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| title_short |
Using self–organizing maps and machine learning models to assess mollusc community structure in relation to physicochemical variables in a West Africa river–estuary system |
| url |
https://doi.org/10.1016/j.ecolind.2021.107706 |
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| author2 |
Odountan, Olaniran Hamed Agboho, Prudenciène Ablawa Dalu, Tatenda Van Bocxlaer, Bert Janssens de Bistoven, Luc Chikou, Antoine Backeljau, Thierry |
| author2Str |
Odountan, Olaniran Hamed Agboho, Prudenciène Ablawa Dalu, Tatenda Van Bocxlaer, Bert Janssens de Bistoven, Luc Chikou, Antoine Backeljau, Thierry |
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| doi_str |
10.1016/j.ecolind.2021.107706 |
| up_date |
2024-07-06T20:04:35.996Z |
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