Machine learning models for short-term rainfall prediction using Uganda’s Lake Victoria Basin Weather Dataset

Abstract

As climate change intensifies, accurate short-term rainfall forecasting has become an urgent research priority. Numerical Weather Prediction models often struggle with precipitation due to high computational requirements and large error margins. This dissertation addresses these challenges by introducing a curated multi-station dataset for the Lake Victoria Basin (LVB) and systematically evaluating Machine Learning and Deep Learning approaches for rainfall forecasting in a data-scarce African environment. Regression experiments benchmarked Random Forest, Support Vector Regression, Neural Network Regression, Least Absolute Shrinkage and Selection Operator, Gradient Boosting, and Extreme Gradient Boosting (XGBoost) Regression. Among these, XGBoost consistently achieved the lowest error, with Mean Absolute Error values as low as 0.006, 0.018, and 0.005 mm h−1 for Uganda, Kenya, and Tanzania, respectively. To complement these continuous forecasts, rainfall classification was implemented using Multi-Layer Perceptrons, Convolutional Neural Networks, and Long Short-Term Memory networks (LSTM). LSTM outperformed alternative architectures, achieving weighted F1-scores above 90% at multiple stations. To overcome data scarcity, a transfer-learning strategy was developed by fine-tuning pre-trained LSTM models from data-rich stations and applying them to the data-limited station of Kisumu, yielding performance improvements of up to 3%. An ensemble of these transfer-learned models using an Exponential Weighting Algorithm further enhanced robustness, delivering gains of up to 5% in F1-score. Overall, the dissertation demonstrates that an ensemble-based transfer-learning framework, grounded in a regionally curated dataset, can substantially improve rainfall forecasting in East Africa. The integration of regression, DL classification, and transfer learning provides methodological advancement and operational potential, contributing to more reliable weather services in data-scarce regions such as the LVB.