Literature DB >> 27454099

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

Cristina Ghinea1, Elena Niculina Drăgoi2, Elena-Diana Comăniţă3, Marius Gavrilescu4, Teofil Câmpean5, Silvia Curteanu6, Maria Gavrilescu7.   

Abstract

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Keywords:  Modeling; Prognosis; Regression; Software; Solid waste; Trend analysis

Mesh:

Substances:

Year:  2016        PMID: 27454099     DOI: 10.1016/j.jenvman.2016.07.026

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  5 in total

1.  Long short-term memory neural network and improved particle swarm optimization-based modeling and scenario analysis for municipal solid waste generation in Shanghai, China.

Authors:  Deyun Wang; Ying-An Yuan; Yawen Ben; Hongyuan Luo; Haixiang Guo
Journal:  Environ Sci Pollut Res Int       Date:  2022-05-14       Impact factor: 5.190

2.  The Impact of Consumption Patterns on the Generation of Municipal Solid Waste in China: Evidences from Provincial Data.

Authors:  Jinhui Liu; Qing Li; Wei Gu; Chen Wang
Journal:  Int J Environ Res Public Health       Date:  2019-05-16       Impact factor: 3.390

3.  Variables Influencing per Capita Production, Separate Collection, and Costs of Municipal Solid Waste in the Apulia Region (Italy): An Experience of Deep Learning.

Authors:  Fabrizio Fasano; Anna Sabrina Addante; Barbara Valenzano; Giovanni Scannicchio
Journal:  Int J Environ Res Public Health       Date:  2021-01-17       Impact factor: 3.390

4.  Root causes of underperforming urban waste services in developing countries: Designing a diagnostic tool, based on literature review and qualitative system dynamics.

Authors:  Hans Breukelman; Harold Krikke; Ansje Löhr
Journal:  Waste Manag Res       Date:  2022-02-08

5.  Barriers and Enablers to Food Waste Recycling: A Mixed Methods Study amongst UK Citizens.

Authors:  Ayşe Lisa Allison; Fabiana Lorencatto; Susan Michie; Mark Miodownik
Journal:  Int J Environ Res Public Health       Date:  2022-02-26       Impact factor: 3.390

  5 in total

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