Forecasting the amount of domestic waste clearance in Shenzhen with an optimized grey model-Reference-Cited by-同舟云学术

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Forecasting the amount of domestic waste clearance in Shenzhen with an optimized grey model

Published:2024-04-03 Issue:7 Volume:38 Page:2711-2729
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Zeng Bo,Xia Chao,Yang Yingjie

Funder

Major project of science and technology research program of Chongqing Education Commission of China

Chongqing Natural Science Foundation of China

Chongqing Graduate Tutor Team Construction Project

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00477-024-02706-2.pdf

Reference45 articles.

1. Alam P, Sharholy M, Khan AH et al (2022) Energy generation and revenue potential from municipal solid waste using system dynamic approach. Chemosphere 299:134351. https://doi.org/10.1016/j.chemosphere.2022.134351

2. Alzamora BR, Barros RTDV, De Oliveira LK, Gonçalves SS (2022) Forecasting and the influence of socioeconomic factors on municipal solid waste generation: a literature review. Environmental Development 44:100734. https://doi.org/10.1016/j.envdev.2022.100734

3. Anjum M, Shahab S, Umar MS (2021) Application of modified grey forecasting model to predict the municipal solid waste generation using MLP and MLE. Int J Math Eng Manag Sci 6:1276–1296. https://doi.org/10.33889/IJMEMS.2021.6.5.077

4. Ayeleru OO, Fajimi LI, Oboirien BO, Olubambi PA (2021) Forecasting municipal solid waste quantity using artificial neural network and supported vector machine techniques: a case study of Johannesburg, South Africa. J Clean Prod 289:125671. https://doi.org/10.1016/j.jclepro.2020.125671

5. Ceylan Z (2021) Short-term prediction of COVID-19 spread using grey rolling model optimized by particle swarm optimization. Appl Soft Comput 109:107592. https://doi.org/10.1016/j.asoc.2021.107592

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