Vu Hoang Lan, Ng Kelvin Tsun Wai, Richter Amy, Kabir Golam
Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Parkway, SK S4S 0A2, Canada.
Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Saskatchewan S4S 0A2, Canada.
Sustain Cities Soc. 2021 Dec;75:103339. doi: 10.1016/j.scs.2021.103339. Epub 2021 Sep 8.
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.
提出了一种新的建模框架，以估计疫情期间加拿大一个省会城市的混合垃圾处理率。利用气候、社会经济和与新冠疫情相关的每日变量，在不同的输入滞后时间和研究期间开发了不同的递归神经网络模型。假设使用不同的时间序列和滞后输入可能会提高建模精度。考虑到2013年1月至2020年9月的整个7.5年期间，多变量工作日模型在测试阶段对滞后时间很敏感。似乎输入变量的选择比垃圾模型的复杂性更重要。应用与新冠疫情相关输入的模型通常表现更好，平均平均绝对百分比误差（MAPE）为10.1%。然而，不同时期的优化滞后时间相似，新冠疫情相关时期的平均滞后略长，为5.3天。输入变量最少的更简单模型似乎能更好地模拟垃圾处理率，“温度-湿度”（Temp-Hum）模型和“温度-新冠新检测病例”（Temp-New Test）模型都能很好地捕捉总体处理趋势，MAPE分别为10.3%和9.4%。在新冠疫情期间，使用分离时间序列输入的好处更为明显，建模误差显著降低。
