Machine learning approaches for imputing missing meteorological data in Senegal

Machine learning approaches for imputing missing meteorological data in Senegal

This study presents the first comprehensive evaluation in West Africa of four imputation methods, Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), and Ordinary Kriging (OK), applied to six core meteorological variables across Senegal over a ten-year period (2015–2024). By sim...

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Bibliographic Details
Main Authors: Mory Toure, Nana Ama Browne Klutse, Mamadou Adama Sarr, Md Abul Ehsan Bhuiyan, Annine Duclaire Kenne, Wassila Mamadou Thiaw, Daouda Badiane, Amadou Thierno Gaye, Ousmane Ndiaye, Cheikh Mbow
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Applied Computing and Geosciences
Subjects:
Missing data
Machine learning
Imputation
Random forest
XGBoost
Climate data quality
Online Access:http://www.sciencedirect.com/science/article/pii/S2590197425000631
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