The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19

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, socio-economic, 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.

[Retracted] Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

“…According to the function and structure of the neural network, the corresponding learning methods have different forms [ 15 , 16 ]. These learning methods have their own advantages for solving all kinds of problems [ 17 , 18 ].…”

“…According to the function and structure of the neural network, the corresponding learning methods have different forms [15,16].…”

Evolution of COVID-19 municipal solid waste disposal behaviors using epidemiology-based periods defined by World Health Organization guidelines

“…Temporal trends based on administrative COVID-19 measures such as lockdown and vaccination protocol ( Ragazzi et al, 2020 , Aurpa 2021 , Vu et al, 2021a ) are helpful in studying short-term behavioral changes such as panic buying, stockpiling, and elevated sense of personal hygiene. However, the use of governmental administrative measures probably overestimated the impact of COVID-19 on solid waste management because administrative periods depend heavily on the economic ( Auray & Eyquem, 2020 ; Fosco & Zurita, 2021 ), social ( Tisdell, 2020 ; Yezli & Khan, 2020 ), and political ( Acedański, 2021 ; Besley & Dray, 2022 ) affluence of the study area.…”

Hospital waste generation during the first wave of COVID-19 pandemic: a case study in Delhi

“…During a pandemic, additional waste collection services and special management measures are typically required to protect public health and the environment (Ilyas et al, 2020;Yang et al, 2021). Studies on different methods on proper monitoring, handling, and treatment of abnormal patterns of waste streams during the pandemic have been recently reported (Purnomo et al, 2021;Richter et al, 2021a;Vu et al 2021a).…”

“…Data accuracy and reliability is a key challenge in the development of an evidence-based waste management system (Richter et al 2019;Ghosh and Ng, 2021), especially during the pandemic. For example, different modeling approaches such as the uses of lagged inputs and distinct time series (Vu et al 2021a) and separated waste fractions (Vu et al 2021b) were attempted to minimize uncertainties in data and variations in waste recycling behaviors during the COVID pandemic. Proper storage, treatment, and disposal of healthcare wastes also require accurate and precise waste data (Fletcher et al, 2021;Manupati et al 2021).…”