Development of machine learning multi-city model for municipal solid waste generation prediction

Highlights

● A database of municipal solid waste (MSW) generation in China was established.

● An accurate MSW generation prediction model (WGMod) was constructed.

● Key factors affecting MSW generation were identified.

● MSW trends generation in Beijing and Shenzhen in the near future are projected.

Abstract

Integrated management of municipal solid waste (MSW) is a major environmental challenge encountered by many countries. To support waste treatment/management and national macroeconomic policy development, it is essential to develop a prediction model. With this motivation, a database of MSW generation and feature variables covering 130 cities across China is constructed. Based on the database, advanced machine learning (gradient boost regression tree) algorithm is adopted to build the waste generation prediction model, i.e., WGMod. In the model development process, the main influencing factors on MSW generation are identified by weight analysis. The selected key influencing factors are annual precipitation, population density and annual mean temperature with the weights of 13%, 11% and 10%, respectively. The WGMod shows good performance with R² = 0.939. Model prediction on MSW generation in Beijing and Shenzhen indicates that waste generation in Beijing would increase gradually in the next 3–5 years, while that in Shenzhen would grow rapidly in the next 3 years. The difference between the two is predominately driven by the different trends of population growth.

Graphical abstract

Keywords

Municipal solid waste / Machine learning / Multi-cities / Gradient boost regression tree

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Acknowledgements

This work was financially supported by the National Key R&D Program of China (Nos. 2018YFD1100600 and 2018YFC1902900). We deeply appreciate our friends for correction of language and advice to this paper.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-022-1551-6 and is accessible for authorized users.

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