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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU145544" target="_blank" >RIV/00216305:26210/22:PU145544 - isvavai.cz</a>

Výsledek na webu

<a href="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0098135422002812" target="_blank" >https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0098135422002812</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compchemeng.2022.107946" target="_blank" >10.1016/j.compchemeng.2022.107946</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computers and Chemical Engineering

ISSN

0098-1354

e-ISSN

1873-4375

Svazek periodika

neuveden

Číslo periodika v rámci svazku

166

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

107946-107946

Kód UT WoS článku

000860379200005

EID výsledku v databázi Scopus

2-s2.0-85136456940