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Smart city article details

Title	Short-Term Time Series Forecasting For Multi-Site Municipal Solid Waste Management
ID_Doc	48702
Authors	Fokker E.; Koch T.; Dugundji E.R.
Year	2023
Published	Procedia Computer Science, 220
DOI	http://dx.doi.org/10.1016/j.procs.2023.03.024
Abstract	Smart containers equipped with ultrasonic sensors at waste and recycle facilities allow waste and recycling companies to build a more efficient and data-driven approach for the collection of municipal solid waste (MSW). In this paper, we propose three time series algorithms that predict the MSW generation of six waste types, using data obtained from smart sensors placed inside 3,640 containers at facilities in six municipalities in the Netherlands. Per neighborhood and per waste type, three models are developed: a Seasonal NaIve Benchmark model, ensemble models of Error, trend, seasonality models with external variables (ETSX), and Quantile Regression models with external variables. According to the RMSE, the ETSX model is the outperforming model for 74% of the time. It is also found that poor weather conditions such as precipitation, wind gusts and thunderstorms result in less waste disposal. The proposed prediction models can be used for more efficient waste collection, in order to collect waste before the fll rate percentage exceeds 100%. In future studies the inclusion of spatial variables and clustering of the containers can be considered. © 2023 Elsevier B.V.. All rights reserved.
Author Keywords	etsx; external variables; prediction models; quantile regression; sensor data; smart cities; solid waste management; time series

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