Machine Learning-Based Forecasting of Waste Generation Proxies Under Data-Limited Conditions for Supporting Adaptive and Sustainable Citarum River Management

This study addresses the prediction of daily waste generation dynamics under data-limited conditions in a strategic watershed serving over 25 million residents. A machine learning framework is developed using daily proxies reconstructed from annual data (2019–2024) through an additive seasonal stochastic disaggregation approach, while maintaining consistency with official SIPSN records. Statistical analysis identifies the 2023 annual total as anomalous (+127.06% YoY) using the IQR method, while sensitivity tests to various parameter configurations indicate that the baseline setting (α = 0.95; σ_frac = 0.08) provides stable estimates. Four models—Random Forest, Support Vector Regression (SVR), XGBoost, and Long Short-Term Memory (LSTM)—are evaluated using strict chronological partitioning to maintain temporal integrity. Results indicate that the evaluation reflects the model’s ability to reproduce synthetic proxies, rather than direct field observations. SVR performed best (R 2 = 0.8157; RMSE = 881.43 t/day), outperforming the persistence baseline by +32.2%. After data leakage correction, XGBoost’s performance decreased significantly (R 2 = 0.1591). Feature analysis confirmed the dominance of short-term statistical indicators, while the hierarchical bootstrap approach produced more comprehensive uncertainty estimates, with SVR remaining the most stable across seasons.