Kingsley Drah*, Nana K. A. Appiah-Badu, Yaw M. Missah and Leonard K. Amekudzi
Ensemble learning has drawn appreciable research attention attributable to its exceptional generalization achievement. In the field of rainfall prediction, many researchers have adopted various machine learning techniques based on varying meteorological parameters. This paper contributes to employing ensemble techniques for rainfall prediction based on seven climatic features form the Ghana meteorological agency covering 1980-2019 for 22 synoptic stations. The experiments involved 6 base algorithms (Logistic regression, decision tree, random forest, extreme gradient boosting, multilayer perceptron and K-nearest neighbor), 3 meta algorithms (Voting, stacking and bagging) and the ensemble learning. More specifically, the ensemble approach in this study focused on the combination of the base classifiers and vote meta classifier. The performance of the models was evaluated based on correlation coefficient, mean absolute error and root mean squared error. Another mode of comparison of the models was dependent on the time taken to build and test the models on the supplied test set. Generally, findings from the study showed that the ensemble learning outperformed both base and meta algorithms.
