Abstract
Managing the waste collection service is a challenge in the fast-growing city context. A key to success in planning the collection is having an accurate prediction of the filling level of the waste containers. In this study we present a solution to the waste generation prediction problem based on recurrent neural networks. Particularly, we introduce a deep neuroevolutionary technique to automatically design a deep network that encapsulates the behavior of all the waste containers in a city. We analyze a real world case study consisting of one year of filling level values of 217 containers located in a city in the south of Spain and compare our results to the state-of-the-art. The results show that the predictions of our approach exceeds all its competitors and that its accuracy is a key enabler for an appropriate waste collection planning.
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Acknowledgements
This research was partially funded by Ministerio de Economía, Industria y Competitividad, Gobierno de España, and European Regional Development Fund grant numbers TIN2016-81766-REDT (http://cirti.es), and TIN2017-88213-R (http://6city.lcc.uma.es), and by Universidad de Málaga, Campus Internacional de Excelencia Andalucía TECH.
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Camero, A., Toutouh, J., Ferrer, J., Alba, E. (2019). Waste Generation Prediction in Smart Cities Through Deep Neuroevolution. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-CITIES 2018. Communications in Computer and Information Science, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-12804-3_15
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