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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Bakici, T., Almirall, E., Wareham, J.: A smart city initiative: the case of Barcelona. J. Knowl. Econ. 4(2), 135–148 (2013)
Ghisellini, P., Cialani, C., Ulgiati, S.: A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems. J. Cleaner Prod. 114, 11–32 (2016)
Tukker, A.: Product services for a resource-efficient and circular economy - a review. J. Cleaner Prod. 97, 76–91 (2015)
Teixeira, J., Antunes, A.P., de Sousa, J.P.: Recyclable waste collection planning-a case study. Eur. J. Oper. Res. 158(3), 543–554 (2004)
Ojha, V.K., Abraham, A., Snášel, V.: Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng. Appl. Artif. Intell. 60, 97–116 (2017)
Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, New York (1996)
Ferrer, J., García-Nieto, J., Alba, E., Chicano, F.: Intelligent testing of traffic light programs: validation in smart mobility scenarios. Math. Prob. Eng. 2016, 1–19 (2016)
Garcia-Nieto, J., Ferrer, J., Alba, E.: Optimising traffic lights with metaheuristics: reduction of car emissions and consumption. In: International Joint Conference on Neural Networks, pp. 48–54 (2014)
Massobrio, R., Toutouh, J., Nesmachnow, S., Alba, E.: Infrastructure deployment in vehicular communication networks using a parallel multiobjective evolutionary algorithm. Int. J. Intell. Syst. 32(8), 801–829 (2017)
Nesmachnow, S., Rossit, D., Toutouth, J.: Comparison of multiobjective evolutionary algorithms for prioritized urban waste collection in Montevideo, Uruguay. Electron. Notes Discrete Math. 69, 93–100 (2018)
Toutouh, J., Rossit, D., Nesmachnow, S.: Computational intelligence for locating garbage accumulation points in urban scenarios. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P.M. (eds.) LION 12 2018. LNCS, vol. 11353, pp. 411–426. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05348-2_34
Camero, A., Toutouh, J., Stolfi, D.H., Alba, E.: Evolutionary deep learning for car park occupancy prediction in smart cities. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P.M. (eds.) LION 12 2018. LNCS, vol. 11353, pp. 386–401. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05348-2_32
Ferrer, J., Alba, E.: BIN-CT: urban waste collection based in predicting the container fill level, Jully 2018
Garvin, B.J., Cohen, M., Dwyer, M.B.: Evaluating improvements to a meta-heuristic search for constrained interaction testing. Empirical Softw. Eng. 16(1), 61–102 (2011)
Sahoo, S., Kim, S., Kim, B.I., Kraas, B., Popov Jr., A.: Routing optimization for waste management. Interfaces 35(1), 24–36 (2005)
Dat, L.Q., Truc Linh, D.T., Chou, S.Y., Yu, V.F.: Optimizing reverse logistic costs for recycling end-of-life electrical and electronic products. Expert Syst. Appl. 39(7), 6380–6387 (2012)
Alagöz, A.Z., Kocasoy, G.: Improvement and modification of the routing system for the health-care waste collection and transportation in İstanbul. Waste Manage. 28(8), 1461–1471 (2008)
Beliën, J., De Boeck, L., Van Ackere, J.: Municipal solid waste collection and management problems: a literature review. Transp. Sci. 48(1), 78–102 (2014)
Xu, L., Gao, P., Cui, S., Liu, C.: A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China. Waste Manage. (New York, N.Y.) 33(6), 1324–1331 (2013)
Cole, C., Quddus, M., Wheatley, A., Osmani, M., Kay, K.: The impact of Local Authorities’ interventions on household waste collection: a case study approach using time series modelling. Waste Manag. (New York, N.Y.) 34(2), 266–272 (2014)
Tung, D.V., Pinnoi, A.: Vehicle routing-scheduling for waste collection in Hanoi. Eur. J. Oper. Res. 125(3), 449–468 (2000)
Sniezek, J., Bodin, L.: Using mixed integer programming for solving the capacitated arc routing problem with vehicle/site dependencies with an application to the routing of residential sanitation collection vehicles. Ann. Oper. Res. 144(1), 33–58 (2006)
Bodin, L., Mingozzi, A., Baldacci, R., Ball, M.: The rollon-rolloff vehicle routing problem. Transp. Sci. 34(3), 271–288 (2000)
Ferrer, J., Alba, E.: BIN-CT: sistema inteligente para la gestión de la recogida de residuos urbanos. In: International Greencities Congress, pp. 117–128 (2018)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Haykin, S.: Neural networks and learning machines. Volume 3. Pearson (2009)
Rumelhart, D., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation. Technical report No. ICS-8506, California Univ San Diego La Jolla Inst for Cognitive Science (1985)
Jaeger, H.: Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the echo state network approach, vol. 5. GMD (2002)
Reed, R., Marks, R., Oh, S.: Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter. IEEE Trans. Neural Networks 6(3), 529–538 (1995)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Bergstra, J., Yamins, D., Cox, D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: International Conference on Machine Learning, pp. 115–123 (2013)
Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: International Conference on Machine Learning, pp. 2342–2350 (2015)
Alba, E., Martí, R.: Metaheuristic Procedures for Training Neural Networks, vol. 35. Springer Science & Business Media, Berlin (2006)
Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)
Miikkulainen, R., et al.: Evolving deep neural networks. arXiv preprint arXiv:1703.00548 (2017)
Morse, G., Stanley, K.O.: Simple evolutionary optimization can rival stochastic gradient descent in neural networks. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO 2016, pp. 477–484. ACM (2016)
Su, X., Yan, X., Tsai, C.L.: Linear regression. Wiley Interdisc. Rev. Comput. Stat. 4(3), 275–294 (2012)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Doerr, C.: Non-static parameter choices in evolutionary computation. In: Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, 15–19 July 2017, Companion Material Proceedings, ACM (2017)
Camero, A., Toutouh, J., Alba, E.: DLOPT: deep learning optimization library. arXiv preprint arXiv:1807.03523, July 2018
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI 2016, pp. 265–283 (2016)
Camero, A., Toutouh, J., Alba, E.: Comparing deep recurrent networks based on the MAE random sampling, a first approach. In: Herrera, F., Damas, S., Montes, R., Alonso, S., Cordón, Ó., González, A., Troncoso, A. (eds.) CAEPIA 2018. LNCS (LNAI), vol. 11160, pp. 24–33. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00374-6_3
Camero, A., Toutouh, J., Alba, E.: Low-cost recurrent neural network expected performance evaluation. arXiv preprint arXiv:1805.07159, May 2018
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-12804-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12803-6
Online ISBN: 978-3-030-12804-3
eBook Packages: Computer ScienceComputer Science (R0)