Waste generation prediction under uncertainty in smart cities through deep neuroevolution
Keywords:Deep neuroevolution, Deep learning, Revolutionary algorithms, Smart cities, Waste collection
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.
T. Bakici, E. Almirall, and J. Wareham, “A smart city initiative: the case of barcelona,” Journal of the Knowledge Economy , vol. 4, no. 2, pp. 135–148, 2013.
P. Ghisellini, C. Cialani, and S. Ulgiati, “A review on circular economy: The expected transition to a balanced interplay of environmental and economic systems,” Journal of Cleaner Production , vol. 114, pp. 11–32, 2016.
A. Tukker, “Product services for a resource-efficient and circular economy - a review,” Journal of Cleaner Production , vol. 97, pp. 76–91, 2015.
J. Teixeira, A. P. Antunes, and J. P. de Sousa, “Recyclable waste collection planning––a case study,” European Journal of Operational Research , vol. 158, no. 3, pp. 543–554, nov 2004.
V. K. Ojha, A. Abraham, and V. Snášel, “Metaheuristic design of feedforward neural networks: A review of two decades of research,” Engineering Applications of Artificial Intelligence , vol. 60, pp. 97 – 116, 2017.
T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms . Oxford university press, 1995.
[¨´´¨´¨7] J. Ferrer, J. García, E. Alba, and F. Chicano, “Intelligent testing of traffic light programs: Validation in smart mobility scenarios,” Mathematical Problems in Engineering , vol. 2016, pp. 1–19, 2016. [Online]. Available: http://www.hindawi.com/journals/mpe/2016/3871046/
J. García, J. Ferrer, and E. Alba, “Optimising traffic lights with metaheuristics: Reduction of car emissions and consumption,” in International Joint Conference on Neural Networks , 2014, pp. 48–54.
R. Massobrio, J. Toutouh, S. Nesmachnow, and E. Alba, “Infrastructure deployment in vehicular communication networks using a parallel multiobjective evolutionary algorithm,” International Journal of Intelligent Systems , vol. 32, no. 8, pp. 801–829, 2017.
S. Nesmachnow, D. Rossit, and J. Toutouth, “Comparison of multiobjective evolutionary algorithms for prioritized urban waste collection in montevideo, uruguay,” Electronic Notes in Discrete Mathematics , 2018.
J. Toutouh, D. Rossit, and S. Nesmachnow, “Computational intelligence for locating garbage accumulation points in urban scenarios,” in International Conference on Learning and Intelligent Optimization, LION 12 , 2018, pp. 1–15.
D. G. Rossit, S. Nesmachnow, and J. Toutouh, “Municipal solid waste management in smart cities: facility location of community bins,” in CongresoIberoamericanodeCiudadesInteligentes(ICSC-CITIES2018) , 2018, pp. 1–14.
A. Camero, J. Arellano, and E. Alba, “Road map partitioning for routing by using a micro steady state evolutionary algorithm,” Engineering Applications of Artificial Intelligence , vol. 71, pp. 155–165, 2018.
A. Camero, J. Toutouh, D. H. Stolfi, and E. Alba, “Evolutionary deep learning for car park occupancy prediction in smart cities,” in Learning and Intelligent OptimizatioN Conference LION , 2018.
Y. Jin and J. Branke, “Evolutionary optimization in uncertain environments,” IEEETransactionsonEvolutionaryComputation , vol. 9, no. 5, pp. 303–317, 2005.
A. Camero, J. Toutouh, J. Ferrer, and E. Alba, “Waste generation prediction in smart cities through deep neuroevolution,” in Smart Cities. ICSC-CITIES 2018 , vol. 978, 2019, pp. 192–204.
J. Ferrer and E. Alba, “(bin-ct): Urban waste collection based in predicting the container fill level,” jul 2018. [Online]. Available: http://arxiv.org/abs/1807.01603
B. J. Garvin, M. Cohen, and M. B. Dwyer, “Evaluating improvements to a meta-heuristic search for constrained interaction testing,” Empirical Software Engineering , vol. 16, no. 1, pp. 61–102, 2011.
S. Sahoo, S. Kim, B. I. Kim, B. Kraas, and A. Popov Jr., “Routing optimization for waste management,” Interfaces , vol. 35, no. 1, pp. 24–36, 2005.
L. Q. Dat, D. T. Truc, S. Y. Chou, and V. F. Yu, “Optimizing reverse logistic costs for recycling end-of-life electrical and electronic products,” Expert Systems with Applications , vol. 39, no. 7, pp. 6380–6387, 2012.
A. Z. Alagöz and G. Kocasoy, “Improvement and modification of the routing system for the health-care waste collection and transportation in istanbul,” Waste Management , vol. 28, no. 8, pp. 1461–1471, 2008.
J. Beliën, L. De Boeck, and J. Van Ackere, “Municipal solid waste collection and management problems: A literature review,” Transportation Science , vol. 48, no. 1, pp. 78–102, feb 2014.
L. Xu, P. Gao, S. Cui, and C. Liu, “A hybrid procedure for msw generation forecasting at multiple time scales in xiamen city, china,” Waste management , vol. 33, no. 6, pp. 1324–31, jun 2013.
C. Cole, M. Quddus, A. Wheatley, M. Osmani, and K. Kay, “The impact of local authorities’ interventions on household waste collection: a case study approach using time series modelling,” Waste management , vol. 34, no. 2, pp. 266–72, feb 2014.
D. V. Tung and A. Pinnoi, “Vehicle routing-scheduling for waste collection in hanoi,” European Journal of Operational Research , vol. 125, no. 3, pp. 449–468, 2000.
J. Sniezek and L. Bodin, “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,” Annals of Operations Research , vol. 144, no. 1, pp. 33–58, apr 2006.
L. Bodin, A. Mingozzi, R. Baldacci, and M. Ball, “The rollon–rolloff vehicle routing problem,” Transportation Science , vol. 34, no. 3, pp. 271–288, 2000.
J. Ferrer and E. Alba, “Bin-ct: sistema inteligente para la gestión de la recogida de residuos urbanos,” in International Greencities Congress , 2018, pp. 117–128.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, p. 436, 2015.
S. Haykin, Neural networks and learning machines . Pearson, 2009, vol. 3.
D. Rumelhart, G. E. Hinton, and R. j. Williams, “Learning internal representations by error propagation,” California Univ San Diego La Jolla Inst for Cognitive Science, Tech. Rep. No. ICS-8506, 1985.
H. Jaeger, Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the echo state network approach . GMD, 2002, vol. 5.
R. Reed, R. Marks, and S. Oh, “Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter,” IEEE Transactions on Neural Networks , vol. 6, no. 3, pp. 529–538, 1995.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014.
J. Bergstra, D. Yamins, and D. Cox, “Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures,” in International Conference on Machine Learning , 2013, pp. 115–123.
R. Jozefowicz, W. Zaremba, and I. Sutskever, “An empirical exploration of recurrent network architectures,” in International Conference on Machine Learning , 2015, pp. 2342–2350.
E. Alba and R. Martí, Metaheuristic Procedures for Training Neural Networks . Springer Science & Business Media, 2006.
X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE , vol. 87, no. 9, pp. 1423–1447, 1999.
R. Miikkulainen and et al ., “Evolving deep neural networks,” arXiv preprint arXiv:1703.00548 , 2017. [Online]. Available: http: //arxiv.org/abs/1703.00548
G. Morse and K. O. Stanley, “Simple evolutionary optimization can rival stochastic gradient descent in neural networks,” in Proc. of the Genetic and Evolutionary Computation Conf. 2016 , 2016, pp. 477–484.
X. Su, X. Yan, and C. L. Tsai, “Linear regression,” Wiley Interdisciplinary Reviews: Computational Statistics , vol. 4, no. 3, pp. 275–294, 2012.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014.
C. Doerr, “Non-static parameter choices in evolutionary computation,” in Genetic and Evolutionary Computation Conference Companion , 2017.
A. Camero, J. Toutouh, and E. Alba, “(dlopt): Deep learning optimization library,” arXiv preprint arXiv:1807.03523 , july 2018.
F. Chollet and et al , “Keras,” https://keras.io, 2015.
M. Abadi and et al ., “Tensorflow: A system for large-scale machine learning,” in 12 th (USENIX) Symposium on Operating Systems Design and Implementation (OSDI 16) , 2016, pp. 265–283.
A. Camero, J. Toutouh, and E. Alba, “Comparing deep recurrent networks based on the mae random sampling, a first approach,” in Conference of the Spanish Association for Artificial Intelligence (CAEPIA) 2018 , 2018, pp. 1–10.
A. Camero, J. Toutouh, and E. Alba, “Low-cost recurrent neural network expected performance evaluation,” arXiv preprint arXiv:1805.07159 , may 2018.
How to Cite
Revista Facultad de Ingeniería, Universidad de Antioquia is licensed under the Creative Commons Attribution BY-NC-SA 4.0 license. The material published in the journal can be distributed, copied and exhibited by third parties if the respective credits are given to the journal. No commercial benefit can be obtained and derivative works must be under the same license terms as the original work.