Performance evaluation of a recurrent deep neural network optimized by swarm intelligent techniques to model particulate matter

References

• Aceves, M., R. Domínguez, J. Pedraza, and J. E. Vargas-Soto. 2020. Evaluation of key parameters using deep convolutional neural networks for airborne pollution (PM10) prediction. Discrete Dyn. Nat. Soc 2020:1–14. doi:10.1155/2020/2792481.
   Web of Science ®Google Scholar
• Becerra, J., M. Aceves, K. Esquivel, and J. C. Pedraza-Ortega. 2020. Airborne particle pollution predictive model using Gated Recurrent Unit (GRU) deep neural networks. Earth Sci. Inf 13(3):821–34. doi:10.1007/s12145-020-00462-9.
   Google Scholar
• Beesley, L., and J. Taylor. 2019. A stacked approach for chained equations multiple imputation incorporating the substantive model. Biometrics doi:10.1111/biom.13372.
   Web of Science ®Google Scholar
• Beni, G., and J. Wang. 1993. Swarm Intelligence in Cellular Robotic Systems. In Robots and Biological Systems: Towards a New Bionics?, ed. P. Dario, G. Sandini, and P. Aebischer, vol. 102. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-58069-7_38.
   Google Scholar
• Bodor, K., M. Micheu, A. Keresztesi M. V. Birsan, I. A. Nita, Z. Bodor, S. Petres, A. Korodi, and R. Szep. 2021. Effects of PM10 and weather on respiratory and cardiovascular diseases in the Ciuc basin. Atmosphere 12(2):289–310. doi:10.3390/atmos12020289.
   Web of Science ®Google Scholar
• Cabrera, M., M. Aceves, J. Ramos, J. M. Ramos-Arreguin, J. E. Vargas-Soto, and E. Gorrostieta-Hurtado. 2019. Parameters influencing the optimization process in airborne particles PM10 using a neuro-fuzzy algorithm optimized with bacteria foraging. Int. J. Intell. Sci 9(3):67–91. doi:10.4236/ijis.2019.93005.
   Google Scholar
• Chakraborty, A., and A. Kar. 2017. Swarm intelligence: A review of algorithms. Nature-inspired computing and optimization 10:479–94. doi:10.1007/978-3-319-50920-4_19.
   Google Scholar
• Chen, K., Y. Zhou, and F. Dai. 2015. A LSTM-based method for stock returns prediction: A case study of China stock market. International Conference on Big Data, 2823–24.
   Google Scholar
• Cheng, J., G. Zhang, Z. Li, and Y. Li. 2011. Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problem. Soft Comput 16(4):597–614. doi:10.1007/s00500-011-0759-3.
   Web of Science ®Google Scholar
• Christidis, T., A. Erickson, A. Pappin, D. L. Crouse, L. L. Pinault, S. A. Weichenthal, J. R. Brook, A. van Donkelaar, P. Hystad, R. V. Martin, and M. Tjepkema. 2019. Low concentrations of fine particle air pollution and mortality in the Canadian Community Health Survey cohort. Environ. Health 18(84):1–16. doi:10.1186/s12940-019-0518-y.
   PubMedGoogle Scholar
• de Ecología IN. 2019. Manual 2: Sistemas de Medición de la Calidad del Aire. March. https://sinaica.inecc.gob.mx/archivo/guias/2/%20-/%20Sistemas/%20de/%20Medici/%C3/%B3n/%20de/%20la/%20Calidad/%20del/%20Aire.pdf.
   Google Scholar
• Deng, W., J. Xu, and X. Zhao. 2019. An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access 7. doi:10.1109/ACCESS.2019.2897580.
   Google Scholar
• Dorigo, M., V. Maniezzo, and A. Coloni. 1996. Ant system: Optimization by a colony of cooperating agents. Trans. Syst. Man Cybern 26 (1):29–41. doi:10.1109/3477.484436.
   PubMed Web of Science ®Google Scholar
• Dorigo, M., and T. Stützle. 2010. Ant colony optimization: Overview and recent advances. Int. Ser. Oper. Res. Manage. Sci 272:311–51. doi:10.1007/978-1-4419-1665-5-8.
   Google Scholar
• Faris, H., A. Al-Zoubi, A. Heidari, I. Aljarah, M. Mafarja, M. A. Hassonah, and H. Fujita. 2019. An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Inf. Fusion 48:67–83. doi:10.1016/j.inffus.2018.08.002.
   Web of Science ®Google Scholar
• Ghosh, M., R. Guha, R. Sarkar, and A. Abraham. 2019. A wrapper-filter feature selection technique based on ant colony optimization. Neural Comput. Appl doi:10.1007/s00521-019-04171-3.
   Web of Science ®Google Scholar
• Government MC. 2021. Air quality monitoring. http://www.aire.cdmx.gob.mx/default.php?opc=/%27ZaBhnmI=/%27.
   Google Scholar
• Gu, F., B. Ma, J. Guo, P. A. Summers, and P. Hall. 2017. Internet of things and big data as potential solutions to the problems in waste electrical and electronic equipment management: An exploratory study. Waste Manage 68:434–48. doi:10.1016/j.wasman.2017.07.037.
   PubMed Web of Science ®Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. Long short-term memory. Neural Comput 9 (8):1735–80. doi:10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Hua, Y., R. Z. Zhao, R. Li, X. Chen, Z. Liu, and H. Zhang. 2019. Deep learning with long short-term memory for time series prediction. Internet Things Sendor Network. doi:10.1109/MCOM.2019.1800155.
   Google Scholar
• Ismkhan, H. 2017. Effective heuristics for ant colony optimization to handle large-scale problems. Swarm Evol. Comput 32:140–49. doi:10.1016/j.swevo.2016.06.006.
   Web of Science ®Google Scholar
• Klambauer, G., T. Unterthiner, and A. Mayr. 2017. Self-normalizing neural networks. Comput. Sci 3. Available from. doi:10.48550/arXiv.1706.02515.
   Google Scholar
• Kumar, A., P. Dash, R. Dash, and R. Bisoi. 2017. Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach. J. King Saud Univ. Comput. Inf. Sci 29(4):536–52. doi:10.1016/j.jksuci.2015.06.002.
   Google Scholar
• Lee, B., B. Kim, and K. Lee. 2014. Air pollution exposure and cardiovascular disease. Toxicol. Res 30(2):71–75. doi:10.5487/tr.2014.30.2.071.
   PubMedGoogle Scholar
• Li, Y., H. Soleimani, and M. Zohal. 2019. An improved ant colony optimization algorithm for the multidepot green vehicle routing problem with multiple objectives. J. Clean. Prod 227:1161–72. doi:10.1016/j.jclepro.2019.03.185.
   Web of Science ®Google Scholar
• Liu, Y., C. Yang, C. Wu, Q. D. Sun and W. Bi. 2019. Threshold changeable secret image sharing scheme based on interpolation polynomial. Multimed. Tools Appl 78(13):18653–67. doi:10.1007/s11042-019-7205-4.
   Web of Science ®Google Scholar
• Mu, H., S. Otani, M. Okamoto, Y. Yokoyama, Y. Tokushima, K. Onishi, T. Hosoda and Y. Kurozawa. 2014. Assessment of effects of air pollution on daily outpatient visits using the air quality index. J. Med. Sci 57 (4):133–36.
   Google Scholar
• Nguyen, B., B. Xue, and M. Zhang. 2020. A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol. Comput 54:100663. doi:10.1016/j.swevo.2020.100663.
   Web of Science ®Google Scholar
• Nikoonahad, A., R. Naserifar, V. Alipour, Poursafar, A., Miri, M.,Ghafari, H. R., and A. Mohammadi. 2017. Assessment of hospitalization and mortality from exposure to PM10 using AirQ modeling in Ilam, Iran. Environ. Sci. Pollut. Res 24 (27):21791–96. doi:10.1007/s11356-017-9794-7.
   PubMed Web of Science ®Google Scholar
• Ning, J., Q. Zhang, C. Zhang, and B. Zhang. 2018. A best-path-updating information-guided ant colony optimization algorithm. Inf. Sci 433(434):142–62. doi:10.1016/j.ins.2017.12.047.
   Google Scholar
• Ordeñez, B., M. Aceves, S. Fernández, J. M. Ramos-Arreguín, and E. Gorrostieta-Hurtado. 2019. An improved particle swarm optimization (PSO): method to enhance modelling of airborne particulate matter (PM10). Evolving Syst 11(4):615–24. doi:10.1007/s12530-019-09263-y.
   Web of Science ®Google Scholar
• Paniri, M., M. Bagher, and H. Nezamabadi-pour. 2020. Ant colony optimization for multi-objective optimization problems. Knowledge-Based Syst 192(15). doi:10.1016/j.knosys.2019.105285.
   Google Scholar
• Pérez-Cirera, V., E. Schmelkes, O. López-Corona, F. Carrera, A. P. García-Teruel and G. Teruel. 2016. Ingreso y calidad del aire en ciudades: ¿Existe una curva de Kuznets para las emisiones del transporte en la Zona Metropolitana del Valle de Mexico? El trimestre económico 85(340):745–764.
   Google Scholar
• Prokuryakov, A. 2017. Intelligent system for time series forecating. Procedia Comput. Sci 103:363–69. doi:10.1016/j.procs.2017.01.122.
   Google Scholar
• RAMA. 2021. Databases automatic air monitoring network. www.aire.cdmx.gob.mx/default.php?opc=/%27aKBh/%27.
   Google Scholar
• Ramírez-Montañez, J., M. A. Fernández, A. T. Arriaga, J. M. Arreguin and G. A. Calderon. 2019. Evaluation of a recurrent neural network LSTM for the detection of exceedances of particles PM10. International Conference on Electrical Engineering, Conputing Science and Automatic Control (CCE), Mexico City, Mexico, September 11–13.
   Google Scholar
• Resche-Rigon, M., and I. White. 2016. Multiple imputation by chained equations for systematically and sporadically missing multilevel data. Stat. Methods Med. Res 1–16. doi:10.1177/0962280216666564.
   Web of Science ®Google Scholar
• Royston, P., and I. White. 2011. Multiple imputation by chained equations (MICE): Implementation in stata. J. Stat. Softw 45 (4). doi:10.18637/jss.v045.i04.
   PubMed Web of Science ®Google Scholar
• Schranz, M., G. Di Caro, T. Schmickl, W. Elmenreich, F. Arvin, A. Şekercioğlu, and M. Sende. 2021. Swarm intelligence and cyber-physical systems: concepts, challenges and future trends. Swarm Evol. Comput 60:100762. https://doi.org/10.1016/j.swevo.2020.100762
   Web of Science ®Google Scholar
• Sotomayor, A., M. Aceves, E. Gorrostieta, C. Pedraza-Ortega, J. M. Ramos-Arreguín, and J. E. Vargas-Soto. 2013. Forecast urban air pollution in Mexico city by using support vector machines: a kernel performance approach. Int. J. Intell. Sci 3(3):126–35. doi:10.4236/ijis.2013.33014.
   Google Scholar
• Tellez, M., D. López, and P. Fernández. 2018. La movilidad en la Ciudad de México: Impactos, conflictos y oportunidades. Academia 10:73–86.
   Google Scholar
• Uthayakumar, J., N. Metawa, K. Shankar, and S. K. Lakshmanaprabu. 2020. Financial crisis prediction model using ant colony optimization. Int. J. Inf. Manage 50:538–56. doi:10.1016/j.ijinfomgt.2018.12.001.
   Web of Science ®Google Scholar
• Wang, H. Z., L. T. Xing, Y. Yang, R. Qu, and Y. Pan. 2016. A modified ant colony optimization algorithm for network coding resource minimization. IEEE Trans. Evol. Comput 20(3):325–42. doi:10.1109/TEVC.2015.2457437.
   Web of Science ®Google Scholar
• Wang, W., and Y. Lu. 2018. Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in assessing rounding model. IOP Conference Series: Materials Science and Engineering, Kuala Lumpur, Malasya. IOP Publishing.
   Google Scholar
• WHO. 2018. Ambient (outdoor) air quality and health. May.
   Google Scholar
• WHO. 2021. WHO air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide, 6–10.
   Google Scholar
• Wulff, J., and L. Jeppesen. 2017. Multiple imputation by chained equations in praxis: Guidelines and review. Electron. J. Bus. Res. Methods 15 (1):41–56.
   Google Scholar
• Xu, S., Y. Liu, and M. Chen. 2017. Optimization of partial collaborative transportation scheduling in supply chain management with 3PL using ACO. Expert Syst. Appl 71:173–91. doi:10.1016/j.eswa.2016.11.016.
   Web of Science ®Google Scholar
• Yan, Y., Y. Li, M. Sun and Z. Wu. 2019. Primary pollutants and air quality analysis for urban air in China: evidence from Shanghai. Sustainability 11(8):2319. doi:10.3390/su11082319.
   Web of Science ®Google Scholar
• Yixiang, X., and Y. Chunning. 2020. Fault recognition of electric servo steering gear based on long and short-term memory neural network. International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece, September. https://doi.org/10.1109/ICUAS48674.2020.9213882
   Google Scholar
• Zhang, D. 2016. A coefficient of determination for generalized linear models. Am. Stat 71 (4):310–16. doi:10.1080/00031305.2016.1256839.
   Web of Science ®Google Scholar
• Zhao, D., L. Liu, F. Yu, A. A. Heidari, M. Wang, D. Oliva, K. Muhammad, and H. Chen. 2020. Ant colony optimization with horizontal and vertical crossover search: fundamental visions for multi-threshold image segmentation. Expert Syst. Appl 114122. doi: 10.1016/j.eswa.2020.114122.
   Web of Science ®Google Scholar
• Zhu, B., S. Ma, R. Xie, J. Chevallier, and Y. M. Wei. 2018. Hilbert spectra and empirical mode decomposition: a multi-scale event analysis method to detect the impact of economic crises on the European carbon market. Comput. Econ 52(1):105–21. doi:10.1007/s10614-017-9664-x.
   Web of Science ®Google Scholar