Keyword search (4,163 papers available)

"Forecasting" Keyword-tagged Publications:

Title Authors PubMed ID
1 Tuning Deep Learning for Predicting Aluminum Prices Under Different Sampling: Bayesian Optimization Versus Random Search Alicia Estefania Antonio Figueroa 41751647
CONCORDIA
2 A Deep Learning-Based Ensemble System for Brent and WTI Crude Oil Price Analysis and Prediction Zhang Y; Lahmiri S; 41294965
JMSB
3 Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting Ahmed U; Mahmood A; Khan AR; Kuhlmann L; Alimgeer KS; Razzaq S; Aziz I; Hammad A; 40185800
PHYSICS
4 Assessing greenhouse gas emissions in Cuban agricultural soils: Implications for climate change and rice (Oryza sativa L.) production Dar AA; Chen Z; Rodríguez-Rodríguez S; Haghighat F; González-Rosales B; 38295640
ENCS
5 A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19 Mahmud TS; Ng KTW; Hasan MM; An C; Wan S; 37274541
ENCS
6 Identifying climate change refugia for South American biodiversity Sales LP; Pires MM; 36919472
BIOLOGY

 

Title:A cross-jurisdictional comparison on residential waste collection rates during earlier waves of COVID-19
Authors:Mahmud TSNg KTWHasan MMAn CWan S
Link:https://pubmed.ncbi.nlm.nih.gov/37274541/
DOI:10.1016/j.scs.2023.104685
Publication:Sustainable cities and society
Keywords:COVID-19Municipal solid waste managementNorth AmericaQuantitative waste forecastingResidential waste collection rateSARIMA
PMID:37274541 Category: Date Added:2023-06-05
Dept Affiliation: ENCS

Description:

There is currently a lack of studies on residential waste collection during COVID-19 in North America. SARIMA models were developed to predict residential waste collection rates (RWCR) across four North American jurisdictions before and during the pandemic. Unlike waste disposal rates, RWCR is relatively less sensitive to the changes in COVID-19 regulatory policies and administrative measures, making RWCR more appropriate for cross-jurisdictional comparisons. It is hypothesized that the use of RWCR in forecasting models will help us to better understand the residential waste generation behaviors in North America. Both SARIMA models performed satisfactorily in predicting Regina's RWCR. The SARIMA DCV model's performance is noticeably better during COVID-19, with a 15.7% lower RMSE than that of the benchmark model (SARIMA BCV). The skewness of overprediction ratios was noticeably different between jurisdictions, and modeling errors were generally lower in less populated cities. Conflicting behavioral changes might have altered the residential waste generation characteristics and recycling behaviors differently across the jurisdictions. Overall, SARIMA DCV performed better in the Canadian jurisdiction than in U.S. jurisdictions, likely due to the model's bias on a less variable input dataset. The use of RWCR in forecasting models helps us to better understand the residential waste generation behaviors in North America and better prepare us for a future global pandemic.





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