Publication detail

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction

HOY, ZX. PHUANG, ZX. FAROOQUE, AA. FAN, Y. WOON, KS.

Original Title

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction

Type

journal article in Web of Science

Language

English

Original Abstract

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that wellperforming ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no onesize-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.

Keywords

Carbon emissions prediction; Artificial intelligence; Greenhouse gas; Hyperparameter optimization; Machine learning; Uncertainty analysis

Authors

HOY, ZX.; PHUANG, ZX.; FAROOQUE, AA.; FAN, Y.; WOON, KS.

Released

1. 3. 2024

Publisher

ELSEVIER SCI LTD

Location

London

ISBN

0269-7491

Periodical

ENVIRONMENTAL POLLUTION

Number

344

State

United Kingdom of Great Britain and Northern Ireland

Pages from

123386

Pages to

123386

Pages count

13

URL

https://www.sciencedirect.com/science/article/pii/S0269749124001003

BibTex

@article{BUT197516,
  author="HOY, ZX. and PHUANG, ZX. and FAROOQUE, AA. and FAN, Y. and WOON, KS.",
  title="Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction",
  journal="ENVIRONMENTAL POLLUTION",
  year="2024",
  number="344",
  pages="13",
  doi="10.1016/j.envpol.2024.123386",
  issn="0269-7491",
  url="https://www.sciencedirect.com/science/article/pii/S0269749124001003"
}