RT Journal Article
T1 Waste generation prediction under uncertainty in smart cities through deep neuroevolution.
A1 Camero Unzueta, Andres
A1 Toutouh-el-Alamin, Jamal
A1 Ferrer-Urbano, Francisco Javier
A1 Alba-Torres, Enrique
K1 Redes neuronales (Informática)
K1 Computación evolutiva
K1 Ciudades - Efectos de las innovaciones tecnológicas
K1 Residuos
AB The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning.
PB Universidad de Antioquia
YR 2019
FD 2019-08-23
LK https://hdl.handle.net/10630/37641
UL https://hdl.handle.net/10630/37641
LA eng
NO Camero, A., Toutouh, J., Ferrer, J., & Alba, E. (2019). Predicción de la producción de residuos con incertidumbre en la ciudad inteligente mediante neuroevolución profunda. Revista Facultad De Ingeniería Universidad De Antioquia, (93), 128–138.
NO 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). European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 799078. Universidad de Málaga, Campus Internacional de Excelencia Andalucía TECH.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026