This study investigates the influence of time-series data structure on model behavior in sewage generation estimation. Annual data from four regions (2017–2023) were analyzed using Random Forest (RF) and Voting Regressor (VR), while structural characteristics were quantified using autocorrelation, mean absolute change rate, and coefficient of variation. The results indicate that model performance varies depending on data structure. Regions with stronger temporal dependency showed more stable model responses, whereas regions with weaker structural consistency exhibited greater variability in outputs. RF tended to be sensitive to localized fluctuations, leading to region-specific variability, while VR maintained more consistent results by reducing individual model bias and variance. These findings demonstrate that model outcomes are influenced not only by algorithmic design but also by the structural properties of the data, emphasizing the importance of incorporating data characteristics into model selection for sewage generation analysis.
Lee et al. (Wed,) studied this question.