Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio

Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio

This research investigates the comparative predictive efficacy of two leading machine learning methodologies, specifically the XGBoost and Random Forest models, in estimating ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions along the east coast of North America. Using an...

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Bibliographic Details
Main Authors: Lulut Alfaris, Anas Noor Firdaus, Ukta Indra Nyuswantoro, Ruben Cornelius Siagian, Aldi Cahya Muhammad, Rohana Hassan, Rodulfo T. Aunzo, Jr., Reza Ariefka
Format: Article
Language:English
Published: Diponegoro University; Association of Indonesian Coastal Management Experts 2024-06-01
Series:Ilmu Kelautan
Subjects:
forecasting
machine learning methods
model performance metrics
predictive accuracy
Online Access:https://ejournal.undip.ac.id/index.php/ijms/article/view/54074
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https://ejournal.undip.ac.id/index.php/ijms/article/view/54074

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