A Hybrid Approach for Short-Term NO2 Forecasting: Case Study of Bay of Algeciras (Spain)

  • Steffanie Van RoodeEmail author
  • Juan Jesus Ruiz-Aguilar
  • Javier González-Enrique
  • Ignacio J. Turias
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)


A hybrid model is proposed in this research in order to forecast concentration values of NO2 with one-hour prediction horizon in the air quality monitoring network of the Bay of Algeciras area (Spain). Air pollution is an important environmental problem these days and it requires control. However, it is not an easy task. The main problem is that air pollution data series are non-lineal and non-stationary. Thus, techniques based on regression and simple models are not able to entirely capture the phenomenon behaviour. A LASSO-ANN hybrid model is proposed. The first step has been to predict the linear part of the time-series performing a least absolute shrinkage and selection operator (LASSO) model. Later, an artificial neural network (ANN) model has been performed to predict the residual sequence, the unexplained part of the LASSO model. The chaotic residual behaviour has been smoothed using an autoregressive moving window and applying the window median. The last step has been to aggregate the predicted NO2 value and its predicted residual. The model has been validated and tested using cross-validation based on R correlation coefficient, MSE, MAE and d index of agreement, and also Friedman test and LSD test. In addition, the proposed approach has been compared to a simple ANN model. The results reveal that hybrid model presents a better performance than a multiple linear regression and also a simple ANN model. The main purpose is to develop a forecasting model capable of capturing the non-linear information of the variable and increase the accuracy of the outputs.


Air pollution forecast Artificial neural networks Hybrid models LASSO 



This work is part of the coordinated research projects TIN2014-58516-C2-1-R and TIN2014-58516-C2-2-R supported by MICINN (Ministerio de Economía y Competitividad - Spain). Monitoring data have been kindly provided by the Environmental Agency of the Andalusian Government.


  1. 1.
    Gong, B., Ordieres-Meré, J.: Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: case study of Hong Kong. Environ. Model Softw. 84, 290–303 (2016)CrossRefGoogle Scholar
  2. 2.
    Jiang, P., Li, C., Li, R., Yang, H.: An innovative hybrid air pollution early-warning system based on pollutants forecasting and Extenics evaluation. Knowl.-Based Syst. 164, 174–192 (2018)CrossRefGoogle Scholar
  3. 3.
    Cabaneros, S.M.S., Calautit, J.K.S., Hughes, B.R.: Hybrid artificial neural network models for effective prediction and mitigation of urban roadside NO2 pollution. Energy Procedia 142, 3524–3530 (2017)CrossRefGoogle Scholar
  4. 4.
    Cheng, S., Li, L., Chen, D., Li, J.: A neural network based ensemble approach for improving the accuracy of meteorological fields used for regional air quality modeling. J. Environ. Manag. 112, 404–414 (2012)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Zhang, Z., et al.: Evolution of surface O3 and PM2.5 concentrations and their relationships with meteorological conditions over the last decade in Beijing. Atmos. Environ. 108, 67–75 (2015)CrossRefGoogle Scholar
  7. 7.
    Turias, I.J., González, F.J., Martin, M.L., Galindo, P.L.: Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy. Environ. Monit. Assess. 143(1–3), 131–146 (2008)CrossRefGoogle Scholar
  8. 8.
    Cisneros, M.A.P., Morán, L.J.M., Arreola, A.G.: Artificial neural networks applied in the forecast of pollutants into the Rio Santiago, based on the sample of a pollutant, by data fusion. In: 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), pp. 1135–1138 (2016)Google Scholar
  9. 9.
    Ardalani-Farsa, M., Zolfaghari, S.: Chaotic time series prediction with residual analysis method using hybrid Elman-NARX neural networks. Neurocomputing 73(13–15), 2540–2553 (2010)CrossRefGoogle Scholar
  10. 10.
    Tibshirani, R.: Regression shrinkage and selection via the lasso: a retrospective. J. R. Stat. Soc. Ser. B Stat. Methodol. 73(3), 273–282 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)CrossRefGoogle Scholar
  12. 12.
    Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propagation. In: Parallel Distributed Processing, pp. 318–362. MIT Press, Cambridge (1986)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Steffanie Van Roode
    • 1
    • 2
    Email author
  • Juan Jesus Ruiz-Aguilar
    • 1
    • 3
  • Javier González-Enrique
    • 1
    • 2
  • Ignacio J. Turias
    • 1
    • 2
  1. 1.Intelligent Modelling of Systems Research Group, Polytechnic School of EngineeringUniversity of CádizAlgecirasSpain
  2. 2.Department of Computer Science Engineering, Polytechnic School of EngineeringUniversity of CádizAlgecirasSpain
  3. 3.Department of Civil and Industrial Engineering, Polytechnic School of EngineeringUniversity of CádizAlgecirasSpain

Personalised recommendations