Forecasting municipal waste accumulation rate and personal consumption expenditures using vector autoregressive (VAR) model

IF 1.9 Q3 ENGINEERING, INDUSTRIAL
J. Bień
{"title":"Forecasting municipal waste accumulation rate and personal consumption expenditures using vector autoregressive (VAR) model","authors":"J. Bień","doi":"10.30657/pea.2022.28.17","DOIUrl":null,"url":null,"abstract":"Abstract Accurate forecasting of municipal solid waste (MSW) generation is important for the planning, operation and optimization of municipal waste management system. However, it’s not easy task due to dynamic changes in waste volume, its composition or unpredictable factors. Initially, mainly conventional and descriptive statistical models of waste generation forecasting with demographic and socioeconomic factors were used. Methods based on machine learning or artificial intelligence have been widely used in municipal waste projection for several years. This study investigates the trend of municipal waste accumulation rate and its relation to personal consumption expenditures based on the yearly data achieved from Local Data Bank (LDB) driven by Polish Statistical Office. The effect of personal consumption expenditures on the municipal waste accumulation rate was analysed by using the vector autoregressive model (VAR). The results showed that such method can be successfully used for this purpose with an approximate level of 2.3% Root Mean Square Error (RMSE).","PeriodicalId":36269,"journal":{"name":"Production Engineering Archives","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Production Engineering Archives","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30657/pea.2022.28.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract Accurate forecasting of municipal solid waste (MSW) generation is important for the planning, operation and optimization of municipal waste management system. However, it’s not easy task due to dynamic changes in waste volume, its composition or unpredictable factors. Initially, mainly conventional and descriptive statistical models of waste generation forecasting with demographic and socioeconomic factors were used. Methods based on machine learning or artificial intelligence have been widely used in municipal waste projection for several years. This study investigates the trend of municipal waste accumulation rate and its relation to personal consumption expenditures based on the yearly data achieved from Local Data Bank (LDB) driven by Polish Statistical Office. The effect of personal consumption expenditures on the municipal waste accumulation rate was analysed by using the vector autoregressive model (VAR). The results showed that such method can be successfully used for this purpose with an approximate level of 2.3% Root Mean Square Error (RMSE).
利用向量自回归(VAR)模型预测城市垃圾积累率和个人消费支出
摘要准确预测城市生活垃圾产生量对城市垃圾管理系统的规划、运行和优化具有重要意义。然而,由于废物体积、成分或不可预测因素的动态变化,这项任务并不容易。最初,主要使用传统的描述性统计模型,结合人口和社会经济因素进行废物产生预测。基于机器学习或人工智能的方法已经在城市垃圾预测中广泛应用了几年。本研究基于波兰统计局推动的地方数据库(LDB)的年度数据,调查了城市垃圾堆积率的趋势及其与个人消费支出的关系。利用向量自回归模型分析了个人消费支出对城市垃圾堆积率的影响。结果表明,这种方法可以成功地用于此目的,其近似水平为2.3%的均方根误差(RMSE)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Production Engineering Archives
Production Engineering Archives Engineering-Industrial and Manufacturing Engineering
CiteScore
6.10
自引率
13.00%
发文量
50
审稿时长
6 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信