A performance and interpretability assessment of machine learning models for rainfall prediction in the Republic of Ireland

Abstract

Rainfall prediction significantly impacts agriculture, water reserves, and preparations for flooding conditions. This research examines the performance and interpretability of machine learning (ML) models for rainfall prediction in the Republic of Ireland. The study uses a brute force approach and the Leave One Feature Out (LOFO) methodology to evaluate model performance under highly correlated variables. Results reveal consistent performance across ML algorithms, with average Area Under the Curve Precision–Recall (AUC-PR) scores ranging from 0.987 to 1.000, with certain features such as atmospheric pressure and soil moisture deficits demonstrating significant influence on prediction outcomes.SHapley Additive exPlanations (SHAP) values provide insights into feature importance, reaffirming the significance of atmospheric pressure and soil moisture deficits in rainfall prediction. This study underscores the importance of feature selection and interpretability in enhancing the accuracy and usability of ML models for rainfall prediction in Ireland.