Machine Learning Models for Climate Prediction and Adaptation in Guinea

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