ArticleSustainable cities and society2021
The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19.
Article in Sustainable cities and society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19.Sustainable cities and society · 2023Article
- Evolution of COVID-19 municipal solid waste disposal behaviors using epidemiology-based periods defined by World Health Organization guidelines.Sustainable cities and society · 2022Article
- COVID-19 impacts on household solid waste generation in six Latin American countries: a participatory approach.Environmental monitoring and assessment · 2022Article
- To what extent do waste management strategies need adaptation to post-COVID-19?The Science of the total environment · 2022Review
- Hospital waste generation during the first wave of COVID-19 pandemic: a case study in Delhi.Environmental science and pollution research international · 2022Article
- Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale.Journal of environmental and public health · 2022Article
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4 authors.
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Abstract
A new modeling framework is proposed to estimate mixed waste disposal rates in a Canadian capital city during the pandemic. Different Recurrent Neural Network models were developed using climatic, socioeconomic, and COVID-19 related daily variables with different input lag times and study periods. It is hypothesized that the use of distinct time series and lagged inputs may improve modeling accuracy. Considering the entire 7.5-year period from Jan 2013 to Sept 2020, multi-variate weekday models were sensitive with lag times in the testing stage. It appears that the selection of input variables is more important than waste model complexity. Models applying COVID-19 related inputs generally had better performance, with average MAPE of 10.1%. The optimized lag times are however similar between the periods, with slightly longer average lag for the COVID-19 at 5.3 days. Simpler models with least input variables appear to better simulate waste disposal rates, and both 'Temp-Hum' (Temperature-Humidity) and 'Temp-New Test' (Temperature-COVID new test case) models capture the general disposal trend well, with MAPE of 10.3% and 9.4%, respectively. The benefits of the use of separated time series inputs are more apparent during the COVID-19 period, with noticeable decrease in modeling error.
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