What does this research mean for the field?

Machine learning models, particularly Random Forest Regression, can moderately accurately predict temperature changes in Ethiopia, contributing to climate adaptation planning. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To create accurate climate prediction models using machine learning algorithms to aid adaptation strategies in Ethiopia.

February 27, 2026Open Access

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

Key Points

To create accurate climate prediction models using machine learning algorithms to aid adaptation strategies in Ethiopia.
Employ machine learning algorithms such as Random Forest Regression and Support Vector Machines.
Utilize data from the Ethiopian Meteorological Agency focusing on temperature, precipitation, and wind speed.
Assess model performance using Mean Absolute Error as a metric.
Random Forest model achieved a Mean Absolute Error of 2.5°C for temperature predictions over five years.
Machine learning models successfully developed and validated to enhance climate adaptation planning.

Abstract

Climate change poses significant challenges to Ethiopia's agricultural productivity and socio-economic development. Accurate climate prediction models are essential for effective adaptation strategies. A suite of machine learning algorithms was employed including Random Forest Regression (RF) and Support Vector Machines (SVM). Data from the Ethiopian Meteorological Agency were used, covering temperature, precipitation, and wind speed. The models' performance was assessed using Mean Absolute Error (MAE) as a metric. The Random Forest model showed an MAE of 2. 5°C for temperature predictions over a five-year period, indicating moderate accuracy in climate prediction. Machine learning models were successfully developed and validated, contributing to improved climate adaptation planning in Ethiopia. Further research should aim at integrating these models into existing agricultural and urban planning systems to enhance their practical utility. Climate Prediction, Machine Learning, Random Forest Regression, Support Vector Machines, Adaptation Planning Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.

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