Estimation of chlorophyll-a in uncrewed aircraft systems imagery using autonomous surface vessel data with machine learning algorithms and feature selection techniques

Chlorophyll-a (Chl-a) is a critical biological indicator of the eutrophic state of water bodies, emphasizing the importance of its detailed characterization and continuous monitoring. This study evaluated the performance of 10 widely used Machine Learning (ML) algorithms in deriving the spatiotempor...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Mohammad Shakiul Islam, Padmanava Dash, Abduselam M. Nur, Hafez Ahmad, Rajendra M. Panda, Jessica S. Wolfe, Gray Turnage, Lee Hathcock, Gary D. Chesser, Jr, Robert J. Moorhead
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Elsevier 2025-03-01
Sarja:	Ecological Informatics
Aiheet:	Chlorophyll-a Uncrewed aircraft systems Autonomous surface vessel Machine learning algorithm Feature selection techniques
Linkit:	http://www.sciencedirect.com/science/article/pii/S1574954124004965
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