A Method of Garbage Quantity Prediction Based on Population Change

Qiumei Yu, Hongjie Wan, Junchen Ma, Huakang Li, Guozi Sun*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Aiming at the problem that the amount of urban waste changes due to population changes and is difficult to predict, a method for predicting the amount of waste based on urban population changes is proposed. After analyzing the correlation between the urban population data of Shanghai and the annual output of garbage from 2000 to 2019, the correlation coefficient and strong correlation data items are calculated. On this basis, judge whether the original population data items meet the conditions of the grey prediction model, and determine whether it can be modeled according to the grey model. Based on the grey theory, this paper analyzes the basic situation of population changes and future population growth trend in Shanghai. Finally, the annual production of municipal solid waste in Shanghai from 2020 to 2025 is predicted based on multiple linear regression analysis. The results show that the waste production of Shanghai has shown a slow growth trend since 2019, and will not increase significantly in the natural state in recent years, which provides a reference basis for subsequent research and analysis.

Original languageEnglish
Title of host publicationIntelligent Information Processing XI - 12th IFIP TC 12 International Conference, IIP 2022, Proceedings
EditorsZhongzhi Shi, Jean-Daniel Zucker, Bo An
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031039478
Publication statusPublished - 2022
Event12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022 - Qingdao, China
Duration: 27 May 202230 May 2022

Publication series

NameIFIP Advances in Information and Communication Technology
Volume643 IFIP
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X


Conference12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022


  • Correlation analysis
  • Grey prediction model
  • Multiple linear regression
  • Population changes
  • Waste production


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