Detail publikace

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.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

1. 10. 2022

Nakladatel

Elsevier Ltd

ISSN

0098-1354

Periodikum

Computers and Chemical Engineering

Číslo

166

Stát

Spojené státy americké

Strany od

107946

Strany do

107946

Strany počet

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"
}