Scholarlink

<p>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 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\((R^2)\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">(</mo> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation> 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Hybrid fuzzy machine learning model for municipal solid waste forecasting in India

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

查看全文
  • Barathi Gnanavelu,
  • Jagadeeswari Murugan

摘要

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.