Assessing the Performance of Ensemble and Regularized Models for Daily Rainfall Forecasting in Singapore

Abstract

This study benchmarks ensemble and regularized machine learning models for daily rainfall forecasting using meteorological data from forty-four observation stations across Singapore. The country’s highly variable tropical climate and frequent short-duration rainfall events pose major challenges for urban flood mitigation and operational forecasting. To address this, three algorithms—Lasso Regression, XGBoost Regression, and Gradient Boosting Regression—were developed and evaluated through a systematic comparison of predictive performance. Each model was trained using data from 1980–2023 and validated on independent observations from 2024–2025. The input variables included sub-hourly rainfall intensity, temperature, and wind-related parameters processed through a standardized data-cleaning and imputation pipeline. Results show that XGBoost achieved the most consistent and accurate predictions, with superior performance under both normal and heavy rainfall conditions. Statistical tests confirmed that the improvement was significant compared to Lasso and Gradient Boosting. These findings demonstrate the effectiveness of ensemble-based approaches for enhancing the reliability of data-driven rainfall forecasting in tropical urban environments and support their integration into early warning and hydrological risk management systems.