Waste generation prediction under uncertainty in smart cities through deep neuroevolution

By Andrés Camero, Jamal Toutouh, Javier Ferrer, Enrique Alba. 2019

14 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Computer Science Applications, Industrial and Manufacturing Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Andrés Camero
2. Enrique Alba
3. Jamal Toutouh
4. Javier Ferrer

In Industrial and Manufacturing Engineering

1. Andrés Camero
2. Enrique Alba

Citations in the corpus

• Bayesian Neural Architecture Search using A Training-Free Performance Metric (2020, background): “Note that the parameters presented in Tables 1 and 2 were aken from [24,37].”
• A Survey on Evolutionary Neural Architecture Search (2020, unknown): “GP [62], [111], [112], [115], [149], [150] [151], [152] [43] [153] ES [123] [44], [45], [57], [154]–[156] [41] [139], [157]”