Development of machine learning - based models to forecast solid waste generation in residential areas: A case study from Vietnam

X. Cuong Nguyen, T. Thanh Huyen Nguyen, D. Duong La, Gopalakrishnan Kumar, Eldon R. Rene, D. Duc Nguyen, S. Woong Chang, W. Jin Chung, X. Hoan Nguyen, V. Khanh Nguyen

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The main aim of this work was to compare six machine learning (ML) - based models to predict the municipal solid waste (MSW) generation from selected residential areas of Vietnam. The input data include eight variables that cover the economy, demography, consumption and waste generation characteristics of the study area. The model simulation results showed that the urban population, average monthly consumption expenditure, and total retail sales were the most influential variables for MSW generation. Among the ML models, the random forest (RF), and k-nearest neighbor (KNN) algorithms show good predictive ability of the training data (80% of the data), with an R2 value > 0.96 and a mean absolute error (MAE) of 121.5–125.0 for the testing data (20% of the data). The developed ML models provided reliable forecasting of the data on MSW generation that will help in the planning, design and implementation of an integrated solid waste management action plan for Vietnam. The limitations of this work may be the heterogeneity of the dataset, such as the lack of data from lower administrative units in the country. In such cases, the predictive ML algorithm can be updated and re-trained in the future when the reliable data is added.

Original languageEnglish
Article number105381
JournalResources, Conservation and Recycling
Volume167
DOIs
Publication statusPublished - 2021 Apr

Bibliographical note

Funding Information:
This research was funded in part by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) (Grant number: 105.99-2019.25 ) and the National Research Foundation (NRF) of Korea (Grant No. 2020R1A2C1101849). The authors also thank their respective organizations for providing infrastructural and staff time support to carry out collaborative research on “Resource Recovery from Wastes”. ERR thanks IHE Delft for providing staff time support under the project “Support to Society”.

Funding Information:
This research was funded in part by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) (Grant number: 105.99-2019.25) and the National Research Foundation (NRF) of Korea (Grant No. 2020R1A2C1101849). The authors also thank their respective organizations for providing infrastructural and staff time support to carry out collaborative research on ?Resource Recovery from Wastes?. ERR thanks IHE Delft for providing staff time support under the project ?Support to Society?.

Publisher Copyright:
© 2020

All Science Journal Classification (ASJC) codes

  • Waste Management and Disposal
  • Economics and Econometrics

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