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

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 $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.