African Software Engineering Review | 28 June 2011
Machine Learning Models for Climate Prediction and Adaptation in Botswana: A Methodological Approach
K, g, o, s, i, M, o, k, g, o, b, o, l, e, ,, M, a, k, g, o, b, a, T, s, h, e, p, i, s, o, ,, B, a, s, w, a, n, a, D, i, t, s, h, w, a, n, e, l, o
Abstract
Climate change poses significant challenges to Botswana's agricultural sector and water resources management. Accurate climate prediction models are essential for effective adaptation planning. We employed a Random Forest model with cross-validation techniques to analyse historical meteorological data from the Botswana Meteorological Services. Uncertainty quantification was achieved using bootstrapping methods. The Random Forest model demonstrated an accuracy of 85% in predicting rainfall patterns, indicating its potential for climate adaptation planning. Our study provides a robust machine learning framework that can enhance Botswana's ability to anticipate and adapt to changing climatic conditions. Government agencies should integrate these models into their decision-making processes for sustainable resource management. Machine Learning, Climate Prediction, Adaptation Planning, Random Forest, Uncertainty Quantification Model estimation used $\hat{\theta}=argmin<em>{\theta}\sum</em>i\ell(y<em>i,f</em>\theta(x<em>i))+\lambda\lVert\theta\rVert</em>2^2$, with performance evaluated using out-of-sample error.