Abstract—The COVID-19 epidemic has profoundly affected urban waste management systems, resulting in variations in garbage creation that complicate traditional forecasting techniques. This study presents an enhanced forecasting framework that combines the Holt-Winters-Confidence Interval (HWCI) projection model with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to augment the precision and dependability of waste generation forecasts. The HWCI model mitigates anomalies and volatile oscillations caused by the pandemic, delivering accurate trend modifications and integrating confidence ranges to resolve uncertainties. The SARIMA model effectively captures both seasonal and nonseasonal fluctuations, facilitating accurate forecasts of garbage generation trends until 2024. The combined HWCI and SARIMA models provide reliable, long-term forecasts, even amidst unforeseen interruptions, enabling waste management agencies to proactively address changing trash generation trends. This research greatly advances urban trash management by improving forecasting approaches that promote sustainability and resilience. Initially, it presents a novel amalgamation of Holt-Winters and SARIMA models, specifically tailored to tackle the intricate and variable characteristics of waste generation data in metropolitan settings. Secondly, the incorporation of confidence intervals offers a dependable method to quantify uncertainties, guaranteeing that predictions remain actionable throughout diverse settings. The study ultimately offers a pragmatic framework that facilitates data-driven decision-making, resource optimization, and longterm strategic planning. This research provides a comprehensive forecasting solution that assists municipalities and urban planners in creating more adaptable and sustainable waste management systems, in accordance with overarching environmental objectives including decreasing resource inefficiencies and mitigating ecological consequences. The proposed strategy emphasizes the necessity of using predictive analytics into urban sustainability efforts, enhancing the cleanliness and resilience of urban environments.

Keywords—COVID-19 impact, waste generation forecasting, holt-winters method, confidence interval projections, SARIMA model, sustainability in waste management

[PDF]

Cite: Atiwat Nanphang, Danupun Visuwan, and Pongchanun Luangpaiboon, &quotInnovative Forecasting in Urban Waste Systems: Enhancing Accuracy and Sustainability with Hybrid Model," International Journal of Environmental Science and Development vol. 16, no. 4, pp. 253-264, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).