Waste generation prediction under uncertainty in smart cities through deep neuroevolution

Andrés Camero; Jamal Toutouh El Alamin; Javier Ferrer; Enrique Alba Torres

Ayuda

Waste generation prediction under uncertainty in smart cities through deep neuroevolution

Andrés Camero ^[1] ; Jamal Toutouh ^[2] ; Javier Ferrer ^[1] ; Enrique Alba ^[1]
1. [1] Universidad de Málaga
  
  Universidad de Málaga
  
  Málaga, España
2. [2] Massachusetts Institute of Technology
  
  Massachusetts Institute of Technology
  
  City of Cambridge, Estados Unidos
Localización: Revista Facultad de Ingeniería: Universidad de Antioquia, ISSN-e 2422-2844, ISSN 0120-6230, Nº. 93, 2019, págs. 128-138
Idioma: inglés
DOI: 10.17533/udea.redin.20190736
Títulos paralelos:
- Predicción de la producción de residuos con incertidumbre en la ciudad inteligente mediante neuroevolución profunda
Enlaces
- Texto completo
Resumen
- español
  El desarrollo insostenible de los países ha creado un problema debido a la imparable generación de residuos. Más aún, la recogida de residuos se realiza siguiendo una ruta predefinida que no tiene en cuenta el nivel real de los contenedores recogidos. Por lo tanto, optimizar la forma en que se recolectan los residuos presenta una oportunidad interesante. En este estudio, abordamos el problema de predecir la tasa de generación de residuos en condiciones reales, es decir, bajo incertidumbre. En particular, utilizamos una técnica neuroevolutiva profunda para diseñar automáticamente una red recurrente que encapsula el nivel de llenado de todos los contenedores de residuos en una ciudad a la vez, y estudiamos la idoneidad de nuestra propuesta cuando nos enfrentamos a datos ruidosos y defectuosos. Validamos nuestra propuesta utilizando un caso real, que consta de más de doscientos contenedores de residuos ubicados en una ciudad de España, y comparamos nuestros resultados con el estado del arte. Los resultados muestran que nuestra propuesta supera a todos sus competidores y que su precisión en un escenario del mundo real, es decir, bajo datos inciertos, es lo suficientemente buena para optimizar la planificación de la recolección de residuos.
- English
  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.
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