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Forecasting municipal solid waste generation using prognostic tools and regression analysis.

Cristina Ghinea1, Elena Niculina Drăgoi2, Elena-Diana Comăniţă3

  • 1"Stefan cel Mare" University of Suceava, Faculty of Food Engineering, 13 Universitatii Street, 720229, Suceava, Romania; "Gheorghe Asachi" Technical University of Iasi, Faculty of Chemical Engineering and Environmental Protection, Department of Environmental Engineering and Management, 73 Prof.Dr.Docent D. Mangeron Str., 700050, Iasi, Romania.

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Summary
This summary is machine-generated.

Accurate municipal solid waste (MSW) forecasting is crucial for effective waste management planning. This study found the S-curve trend model most suitable for predicting MSW generation and composition in Iasi, Romania.

Keywords:
ModelingPrognosisRegressionSoftwareSolid wasteTrend analysis

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Area of Science:

  • Environmental Science
  • Waste Management
  • Predictive Modeling

Background:

  • Effective waste management systems rely on accurate waste generation forecasts.
  • Predictive models offer reliable support for decision-making in waste management.
  • Various factors influence waste generation trends, necessitating robust forecasting methods.

Purpose of the Study:

  • To forecast municipal solid waste (MSW) generation and composition.
  • To identify the most suitable prognostic model for MSW prediction.
  • To analyze the impact of demographic and urban factors on waste generation.

Main Methods:

  • Application of the Waste Prognostic Tool.
  • Utilizing regression analysis and time series analysis.
  • Input variables included population, age, life expectancy, and total MSW.

Main Results:

  • Regression equations were developed for six distinct waste fractions (paper, plastic, metal, glass, biodegradable, other).
  • Accuracy measures confirmed the S-curve trend model's superiority for MSW prediction.
  • The study provides a case study for Iasi, Romania.

Conclusions:

  • The S-curve trend model is the most effective for predicting municipal solid waste generation and composition.
  • Accurate forecasting supports better planning and decision-making in waste management systems.
  • Demographic and urban indicators are key inputs for reliable waste prognostication.