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.
Anglický 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
en
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.
Anglický 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 anglicky
Artificial neural network; Circular economy; Correlation analysis; Hyperparameter optimisation; Waste prediction
Vydáno
01.10.2022
Nakladatel
Elsevier Ltd
ISSN
0098-1354
Číslo
166
Strany od–do
107946–107946
Počet stran
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",
year="2022",
number="166",
month="October",
pages="107946--107946",
publisher="Elsevier Ltd",
issn="0098-1354"
}