Publication detail

Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation

Hoy, ZX., Woon, K.S., Chin, W.C., Hashim, H., Fan, Y.V.

Original Title

Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation

Type

journal article in Web of Science

Language

English

Original Abstract

Future projections of municipal solid waste (MSW) generation trends can resolve data inadequacy in formulating a sustainable MSW management framework. Artificial neural network (ANN) has been recently adopted to forecast MSW generation, but the reliability and validity of the stochastic forecast are not thoroughly studied. This research develops Bayesian-optimised ANN models coupling ensemble uncertainty analysis to forecast country-scale MSW physical composition trends. Pearson correlation analysis shows that each MSW physical composition exhibits collinearity with different indicators; therefore, the MSW should be forecasted based on its heterogeneity. The Bayesian-optimised ANN models forecast with smaller relative standard deviations (3.64–27.7%) than the default ANN models (11.1–44,400%). Malaysia is expected to generate 42,873 t/d of MSW in 2030, comprising 44% of food waste. This study provides a well-generalised ANN framework and valuable insights for the waste authorities in developing a circular economy via proper waste management.

Keywords

Artificial neural network; Circular economy; Correlation analysis; Hyperparameter optimisation; Waste prediction

Authors

Hoy, ZX., Woon, K.S., Chin, W.C., Hashim, H., Fan, Y.V.

Released

1. 10. 2022

Publisher

Elsevier Ltd

ISBN

0098-1354

Periodical

Computers and Chemical Engineering

Number

166

State

United States of America

Pages from

107946

Pages to

107946

Pages count

10

URL

https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0098135422002812

BibTex

@article{BUT179146,
  author="Yee Van {Fan}",
  title="Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation",
  journal="Computers and Chemical Engineering",
  year="2022",
  number="166",
  pages="107946--107946",
  doi="10.1016/j.compchemeng.2022.107946",
  issn="0098-1354",
  url="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0098135422002812"
}