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.

English title

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

Type

WoS Article

Language

en

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 in English

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

Released

2022-10-01

Publisher

Elsevier Ltd

ISSN

0098-1354

Number

166

Pages from–to

107946–107946

Pages count

10

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 & 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"
}