A SO2 Pollution Concentrations Prediction Approach Using Autoencoders

  1. Rodríguez-García, M. I. 1
  2. González-Enrique, J. 1
  3. Ruiz-Aguilar, J. J. 1
  4. Turias, I. J. 1
  1. 1 Higher Technical Engineering School, University of Cádiz, Avd. Ramón Puyol s/n, 11202, Algeciras, (Cádiz), Spain
Actas:
17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)

ISSN: 2367-3370,  2367-3389

ISBN: 9783031180491,  9783031180507

Año de publicación: 2022

Páginas: 41-50

Tipo: Aportación congreso

DOI: 10.1007/978-3-031-18050-7_5 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

To achieve the prediction of SO2(t + 1) concentration values in the area of the Bay of Algeciras, Autoencoders (AE) and Sparse Autoencoders (SAE) have been applied to analyse air quality in this complex zone. A three-year hourly database of air pollutants, meteorological and vessel data were used to test different prediction scenarios. The data were divided into disjoint quartiles (Q1–Q4). AE models are better performed in the medium values (quartiles Q2 and Q3) and SAE models produce equivalent results in low and high values (Q1 and Q4). The results show that AE layers can be stacked to configure a more complex network with different levels of the sparsity of dimensions, together with a final supervised layer for the prediction of the index of the SO2 level (quartiles Q1–Q4).

Referencias bibliográficas

  • González-Enrique, J., Turias, I.J., Ruiz-Aguilar, J.J., Moscoso-López, J.A., Franco, L.: Spatial and meteorological relevance in NO2 estimations: a case study in the Bay of Algeciras (Spain). Stoch. Environ. Res. Risk Assess. 33, 801–815 (2019)
  • González-Enrique, J., Ruiz-Aguilar, J.J., Moscoso-López, J.A., Urda, D., Turias, I.J.: A comparison of ranking filter methods applied to the estimation of NO2 concentrations in the Bay of Algeciras (Spain). Stoch. Env. Res. Risk Assess. 35, 1999–2019 (2021)
  • Moscoso-López, J.A., Urda, D., González-Enrique, J., Ruiz-Aguilar, J.J., Turias, I.J.: Hourly air quality index (AQI) forecasting using machine learning methods. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds.) SOCO 2020. AISC, vol. 1268, pp. 123–132. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-57802-2_12
  • Rodríguez-García, M.I., González-Enrique, J., Moscoso-López, J.A., Ruiz-Aguilar, J.J., Rodríguez-López, J., Turias, I.J.: Comparison of maritime transport influence of SO2 levels in Algeciras and Alcornocales Park (Spain). In: XIV Conference on Transport Engineering, CIT2021, vol. 58, pp. 2352–1465 (2021)
  • Akin, Y., Cansu, Z., Oktay, H.: Air pollution modelling with deep learning: a review. Int. J. Environ. Pollut. Environ. Model. 1(3), 58–62 (2018)
  • Li, X., Peng, L., Hu, Y., Shao, J., Chi, T.: Deep learning architecture for air quality predictions. Environ. Sci. Pollut. Res. 23, 22048–22417 (2016)
  • Bo Zhang, B., Zhang, H., Zhao, G., Lian, J.: Constructing a PM2.5 concentration prediction model by combining autoencoder with Bi-LSTM neural networks. Environ. Model. Softw. 124 (2020)
  • Liao, Q., Zhu, M., Wu, L., Pan, X., Tang, X., Wang, Z.: Deep learning for air quality forecasts: a review. Curr. Pollut. Rep. 6, 399–499 (2020)
  • Mengara, A.G., Park, E., Jang, J., Yoo, Y.: Attention-based distributed deep learning model for air quality forecasting. Sustainability 14(6), 3269 (2022)
  • LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
  • Baldorj, B., Tsagaan, M., Sereeter, L., Bulkhbai, A.: Embedded generative air pollution model with variational autoencoder and environmental factor effect in Ulaanbaatar city. Atmosphere 13(1), 71 (2022)
  • Xayasouk, T., Lee, H.: Air pollution prediction system using deep learning. WIT Trans. Ecol. Environ. 230 (2018)
  • Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
  • Baldi, P., Lu, Z.: Complex-valued autoencoders. Neural Netw. 33, 136–147 (2012)
  • Makhzani, A., Frey, B.: k-Sparse autoencoders. In: 2nd International Conference on Learning Representations, ICLR 2014 – Conference Track Proceedings (2014)
  • Tino, P., Benuskova, L., Sperduti, A.: Artificial Neural Network Models, pp. 455–471. Springer Handbook of Computational Intelligence (2015)
  • Bland, J.M., Altman, D.G.: Multiple significance tests: the Bonferroni method. BMJ 310(6973), 170 (1995)