Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F24%3APU156173" target="_blank" >RIV/00216305:26210/24:PU156173 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0269749124001003" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0269749124001003</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.envpol.2024.123386" target="_blank" >10.1016/j.envpol.2024.123386</a>

Result language

angličtina

Original language name

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction

Original language description

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that wellperforming ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no onesize-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review a

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10500 - Earth and related environmental sciences

Project

<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>

Continuities

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

Publication year

2024

Confidentiality

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

Name of the periodical

ENVIRONMENTAL POLLUTION

ISSN

0269-7491

e-ISSN

1873-6424

Volume of the periodical

neuveden

Issue of the periodical within the volume

344

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

123386-123386

UT code for WoS article

001176892600001

EID of the result in the Scopus database

2-s2.0-85183293256