TY - JOUR
T1 - Impact of COVID-19 on Household Waste Generation
T2 - An Empirical Study Using Machine Learning-based Neural Network
AU - Choi, Sung Won
AU - Lee, Jai Young
N1 - Publisher Copyright:
© 2023, Korea Society of Waste Management. All rights reserved.
PY - 2023
Y1 - 2023
N2 - An accurate forecasting of waste generation is important to establish an efficient waste management. Although the patterns of waste generation are changing due to the COVID-19 pandemic, which started in 2020, there has been no accurate analysis and evaluation. Therefore, this study aims to assess the effects of COVID-19 on household waste generation using machine learning-based neural networks (ANN, LSTM, RNN, and GRU). This study partitioned data into training data (January 2017 – June 2021) and verification data (July 2021 – May 2022) and predicted future waste generation for June 2022 – December 2028. The results showed excellent prediction performance except for high-volatility recyclable waste. Forecasting based on household waste not by type can guarantee predictive performance. In addition, the impact of COVID-19 on household waste generation was a 4.65% increase in garbage waste, a 9.4% increase in food waste, and a 15.53% decrease in recyclable waste.
AB - An accurate forecasting of waste generation is important to establish an efficient waste management. Although the patterns of waste generation are changing due to the COVID-19 pandemic, which started in 2020, there has been no accurate analysis and evaluation. Therefore, this study aims to assess the effects of COVID-19 on household waste generation using machine learning-based neural networks (ANN, LSTM, RNN, and GRU). This study partitioned data into training data (January 2017 – June 2021) and verification data (July 2021 – May 2022) and predicted future waste generation for June 2022 – December 2028. The results showed excellent prediction performance except for high-volatility recyclable waste. Forecasting based on household waste not by type can guarantee predictive performance. In addition, the impact of COVID-19 on household waste generation was a 4.65% increase in garbage waste, a 9.4% increase in food waste, and a 15.53% decrease in recyclable waste.
KW - Forecasting model
KW - Household waste generation
KW - Machine learning
KW - Neural Network
UR - http://www.scopus.com/inward/record.url?scp=85196825516&partnerID=8YFLogxK
U2 - 10.9786/kswm.2023.40.1.12
DO - 10.9786/kswm.2023.40.1.12
M3 - Article
AN - SCOPUS:85196825516
SN - 2093-2332
VL - 40
SP - 12
EP - 20
JO - Journal of Korea Society of Waste Management
JF - Journal of Korea Society of Waste Management
IS - 1
ER -