What question did this study set out to answer?

To explore machine learning techniques for predicting climate impacts on agriculture in Uganda.

February 22, 2026Open Access

Machine Learning Models for Climate Prediction and Adaptation in Uganda: A Methodological Approach

Key Points

To explore machine learning techniques for predicting climate impacts on agriculture in Uganda.
Employed ensemble machine learning techniques including Random Forest and GBM
Modeled climate data to assess prediction accuracy
Utilized model estimation methods to optimize predictions
Achieved an average prediction accuracy of 85% with GBM
Provided valuable insights into climate change impacts on agriculture
Suggested enhanced strategies for agricultural resilience and water management

Abstract

Uganda faces significant climate variability, impacting agriculture, water resources, and public health. Employed ensemble machine learning techniques including Random Forest and Gradient Boosting Machines (GBM). Modelled climate data showed an average prediction accuracy of 85% with GBM, indicating a reliable method for future projections. The models provide valuable insights into climate change impacts on agriculture in Uganda. Implement these models to enhance agricultural resilience and water management strategies. Machine Learning, Climate Prediction, Adaptation Planning, Uganda Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.

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