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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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 |
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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 |
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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. |
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Hoy, Zheng Xuan Woon, Kok Sin Chin, Wen Cheong Hashim, Haslenda Fan, Yee Van |
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Hoy, Zheng Xuan Woon, Kok Sin Chin, Wen Cheong Hashim, Haslenda Fan, Yee Van |
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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 |
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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 |
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Elsevier Ltd |
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2022 |
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http://eprints.utm.my/103391/ http://dx.doi.org/10.1016/j.compchemeng.2022.107946 |
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