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

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...

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Main Authors: Hoy, Zheng Xuan, Woon, Kok Sin, Chin, Wen Cheong, Hashim, Haslenda, Fan, Yee Van
Format: Article
Published: Elsevier Ltd 2022
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Online Access:http://eprints.utm.my/103391/
http://dx.doi.org/10.1016/j.compchemeng.2022.107946
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spelling my.utm.1033912023-11-14T04:03:54Z http://eprints.utm.my/103391/ Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation Hoy, Zheng Xuan Woon, Kok Sin Chin, Wen Cheong Hashim, Haslenda Fan, Yee Van TP Chemical technology 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. Elsevier Ltd 2022 Article PeerReviewed Hoy, Zheng Xuan and Woon, Kok Sin and Chin, Wen Cheong and Hashim, Haslenda and Fan, Yee Van (2022) Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation. Computers and Chemical Engineering, 166 (NA). pp. 1-10. ISSN 0098-1354 http://dx.doi.org/10.1016/j.compchemeng.2022.107946 DOI : 10.1016/j.compchemeng.2022.107946
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TP Chemical technology
spellingShingle TP Chemical technology
Hoy, Zheng Xuan
Woon, Kok Sin
Chin, Wen Cheong
Hashim, Haslenda
Fan, Yee Van
Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation
description 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.
format Article
author Hoy, Zheng Xuan
Woon, Kok Sin
Chin, Wen Cheong
Hashim, Haslenda
Fan, Yee Van
author_facet Hoy, Zheng Xuan
Woon, Kok Sin
Chin, Wen Cheong
Hashim, Haslenda
Fan, Yee Van
author_sort Hoy, Zheng Xuan
title Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation
title_short Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation
title_full Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation
title_fullStr Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation
title_full_unstemmed Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation
title_sort forecasting heterogeneous municipal solid waste generation via bayesian-optimised neural network with ensemble learning for improved generalisation
publisher Elsevier Ltd
publishDate 2022
url http://eprints.utm.my/103391/
http://dx.doi.org/10.1016/j.compchemeng.2022.107946
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