Climate change poses significant challenges to Guinea's agricultural productivity and socio-economic development. Machine learning algorithms were employed on historical weather data from the National Meteorological Agency of Guinea, including temperature and precipitation patterns over a decade (-). The machine learning models achieved an R-squared value of 0. 78 for predicting temperature variations and 0. 65 for rainfall predictions. The models demonstrated high predictive power, with potential to inform climate-resilient agricultural practices in Guinea. Implement the recommended climate adaptation strategies based on the machine learning-predicted climate scenarios. Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
Traore et al. (Sun,) studied this question.