Year: 2026
Type: article
Abstract: Accurate forecasting of municipal solid waste (MSW) generation at the state level is essential for effective planning and policy formulation in India. This study presents a hybrid fuzzy–machine learning framework for short-term, one-step-ahead forecasting of state-level MSW generation under data uncertainty. The approach integrates fuzzy membership–based feature representation as an uncertainty-aware feature engineering step with regression-based machine learning models. Trapezoidal fuzzy membership functions are employed to represent uncertainty in historical MSW data compiled for Indian states over a five-year period, and the resulting fuzzified dataset is used to train Random Forest, Support Vector Regression, and Extreme Gradient Boosting models. Model performance is evaluated using standard five-fold cross-validation. The results indicate that incorporating fuzzy-derived membership degrees leads to a modest but consistent improvement in forecasting accuracy, with the best-performing model achieving a coefficient of determination $$(R^2)$$ ( R 2 ) of 0.766 and a root mean square error of 2382.42 tonnes per day, providing an uncertainty-aware, interpretable decision-support tool for short-term state-level MSW forecasting.
Source: Discover Environment
Authors: Barathi Gnanavelu, Jagadeeswari Murugan
Institution: Vellore Institute of Technology University
Language: English
Subfield: Waste Management and Disposal
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,390
Funder: VIT University
OpenAlex ID: W7170044138