Skip to main content

Advertisement

SpringerLink
Log in

An improved particle swarm optimization (PSO): method to enhance modeling of airborne particulate matter (PM10)

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

Nowadays, it is of paramount importance for human health the monitoring and modelling of air quality. Among the different pollutants, there are some that are considerable more difficult to model due to their chemical composition. Some of these are particulate matter (particles ≤ 10 microns, PM10, and particles ≤ 2.5 microns, PM2.5), which can cause respiratory diseases or even cause premature deaths. Furthermore, There are several models that can be used to evaluate air quality. In this contribution, the combination of a neuro-fuzzy based method with particle swarm optimization is proposed to crucially increase accuracy when dealing with the non-linear behavior of airborne particulate matter (PM10). Several experiments were carried out to show the feasibility of the proposed method and to show that even when the nature of the data, that has dynamic behavior, variance in the spread of data, the present of outliers, climatic conditions, among other factors, the modeling of this particular set of data may be made accurately, showing the robustness and feasibility of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

(Source: SEMARNAT, Mexico—Environment Secretariat)

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Abdulshahed AM, Longstaff AP, Fletcher S (2015) The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Appl Soft Comput 27:158–168

    Article  Google Scholar 

  • Aguirre-Salado AI, Vaquera-Huerta H, Aguirre-Salado CA, Reyes-Mora S, Olvera-Cervantes AD, Lancho-Romero GA, Soubervielle-Montalvo C (2017) Developing a hierarchical model for the spatial analysis of PM10 pollution extremes in the Mexico City metropolitan area. Int J Environ Res Public Health 14(7):734

    Article  Google Scholar 

  • Ahmed H, Glasgow J (2012) Swarm intelligence: concepts, models and applications. School of computing, Queens University Technical Report

  • Angelov P, A generalized approach to fuzzy optimization. Int J Intell Syst 9 (3), 261–268

  • Angelov P, Kasabov N, Evolving computational intelligence systems. In: Proceedings of the 1st international workshop on genetic fuzzy systems, pp. 76–82

  • Angelov P, Yager R, Density-based averaging–a new operator for data fusion. Inf Sci 222, 163–174

  • Angelov P, Sadeghi-Tehran P, Ramezani R, An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi–Sugeno fuzzy systems. Int J Intell Syst 26 (3), 189–205

  • Aránguez E, Ordóñez JM, Serrano J, Aragonés N, Fernández-Patier R, Gandarillas A, Galán I (1999) Contaminantes atmosféricos y su vigilancia. Revista española de salud pública 73:123–132

    Article  Google Scholar 

  • Bagheri A, Peyhani HM, Akbari M (2014) Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Syst Appl 41(14):6235–6250

    Article  Google Scholar 

  • Baruah RD, Angelov P, Evolving local means method for clustering of streaming data, 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8

  • Chen MY (2013) A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering. Inf Sci 220:180–195

    Article  Google Scholar 

  • Collazo-Cuevas JI, Aceves-Fernandez MA, Gorrostieta-Hurtado E, Pedraza-Ortega JC, Sotomayor-Olmedo A, Delgado-Rosas M (2010, February) Comparison between Fuzzy C-means clustering and Fuzzy Clustering Subtractive in urban air pollution. In Electronics, Communications and Computer (CONIELECOMP), 2010 20th International Conference on (pp. 174–179). IEEE

  • El-Abd M, Hassan H, Anis M, Kamel MS, Elmasry M (2010) Discrete cooperative particle swarm optimization for FPGA placement. Appl Soft Comput 10(1):284–295

    Article  Google Scholar 

  • Estrada AL, Aceves-Fernández MA (2015) Design and Implementation of ant colony algorithms to enhance airborne pollution models. Int J Environ Sci Toxicol 2:22–28

    Google Scholar 

  • Koo YS, Choi DR, Kwon HY, Jang YK, Han JS (2015) Improvement of PM10 prediction in East Asia using inverse modeling. Atmos Environ 106:318–328

    Article  Google Scholar 

  • Li Q (2017) NOx reduction based on an improved orthogonal particle swarm optimization. J Residuals Sci Technol 14(3):605–619

    Google Scholar 

  • Li C, Zuo D (2009, March) Fuzzy Multi-objective Particle Swarm Optimization Algorithm Using Industrial Purified Terephthalic Acid Solvent Dehydration Process. In 2009 World Congress on Computer Science and Information Engineering (pp. 215–219). IEEE

  • Mandel JH, Wendt C, Lo C, Zhou G, Hertz M, Ramachandran G (2015) Ambient air pollution and lung disease in China: health effects, study design approaches and future research. Front Med 9(3):392–400

    Article  Google Scholar 

  • Martinez-Zeron E, Aceves-Fernandez MA, Gorrostieta-Hurtado E, Sotomayor-Olmedo A, Ramos-Arreguín JM (2014) Method to improve airborne pollution forecasting by using ant colony optimization and neuro-fuzzy algorithms. Int J Intell Sci 4(04):81

    Article  Google Scholar 

  • Merkle D, Blum C (2008) Swarm intelligence: introduction and application, Springer, New York, p 286 (ISBN: 978-3540740889)

    MATH  Google Scholar 

  • Mohammadi V, Ghaemi S, Kharrati H (2018) PSO tuned FLC for full autopilot control of quadrotor to tackle wind disturbance using bond graph approach. Appl Soft Comput 65:184–195

    Article  Google Scholar 

  • Molina Esquivel E, Brown Colás LA, Prieto Díaz V, Gorbea B, M., & Cuéllar Luna L (2001) Crisis de asma y enfermedades respiratorias agudas: Contaminantes atmosféricos y variables meteorológicas en Centro Habana. Revista Cubana de Medicina General Integral 17(1):10–20

    Google Scholar 

  • Peel JL, Klein M, Flanders WD, Mulholland JA, Freed G, Tolbert PE (2011) Ambient air pollution and apnea and bradycardia in high-risk infants on home monitors. Environ Health Perspect 119(9):1321

    Article  Google Scholar 

  • Selakov A, Cvijetinović D, Milović L, Mellon S, Bekut D (2014) Hybrid PSO–SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank. Appl Soft Comput 16:80–88

    Article  Google Scholar 

  • Siwek K, Osowski S (2016) Data mining methods for prediction of air pollution. Int J Appl Math Comput Sci 26(2):467–478

    Article  MathSciNet  Google Scholar 

  • Srisukkham W, Zhang L, Neoh SC, Todryk S, Lim CP (2017) Intelligent leukaemia diagnosis with bare-bones PSO based feature optimization. Appl Soft Comput 56:405–419

    Article  Google Scholar 

  • Subashini P, Krishnaveni M, Manjutha M (2016, October) Optimized boundary detection algorithm for postal signs recognition system using variant based Particle Swarm intelligence. In Computation System and Information Technology for Sustainable Solutions (CSITSS), International Conference on (pp. 162–166). IEEE

  • Subasi A (2013) Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med 43(5):576–586

    Article  Google Scholar 

  • Tomera M (2015, June) Swarm intelligence applied to identification of nonlinear ship steering model. In Cybernetics (CYBCONF), 2015 IEEE 2nd International Conference on (pp. 133–139). IEEE

  • Vanos JK, Hebbern C, Cakmak S (2014) Risk assessment for cardiovascular and respiratory mortality due to air pollution and synoptic meteorology in 10 Canadian cities. Environ Pollut 185:322–332

    Article  Google Scholar 

  • Vega E, Lowenthal D, Ruiz H, Reyes E, Watson JG, Chow JC, … Alastuey A (2009) Fine particle receptor modeling in the atmosphere of Mexico City. J Air Waste Manag Assoc 59(12):1417–1428

    Article  Google Scholar 

  • Wang L, Yang B, Orchard J (2016) Particle swarm optimization using dynamic tournament topology. Appl Soft Comput 48:584–596

    Article  Google Scholar 

  • Wei LY (2016) A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl Soft Comput 42:368–376

    Article  Google Scholar 

  • Zhang K, Luo M (2015) Outlier-robust extreme learning machine for regression problems. Neurocomputing 151:1519–1527

    Article  Google Scholar 

  • Zhang Y, Wang S, Phillips P, Ji G (2014) Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl Based Syst 64:22–31

    Article  Google Scholar 

  • Zhou X, Angelov P (2007) Autonomous visual self-localization in completely unknown environment using evolving fuzzy rule-based classifier. In: 2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA, pp. 131–138

  • Zhou S, Li W, Qiao J (2017) Prediction of PM2. 5 concentration based on recurrent fuzzy neural network. In Control Conference (CCC), 2017 36th Chinese (pp. 3920–3924). IEEE

Download references

Acknowledgements

The authors would like to acknowledge the financial support of the Mexican government via The National Council of Science and Technology (CONACYT) funding, the enormous help from the academic department of “Scientific and Technological Computation” for their invaluable help.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, 76000, Querétaro, Mexico

    B. Ordóñez-De León, M. A. Aceves-Fernandez, J. M. Ramos-Arreguín & E. Gorrostieta-Hurtado

  2. Department of Computer Systems, Instituto Tecnológico de Querétaro, Av. Tecnológico s/n, Centro, 76000, Santiago de Querétaro, Mexico

    S. M. Fernandez-Fraga

Authors
  1. B. Ordóñez-De León
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. A. Aceves-Fernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. M. Fernandez-Fraga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. M. Ramos-Arreguín
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. Gorrostieta-Hurtado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. A. Aceves-Fernandez.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest at present regarding the publication of this paper.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ordóñez-De León, B., Aceves-Fernandez, M.A., Fernandez-Fraga, S.M. et al. An improved particle swarm optimization (PSO): method to enhance modeling of airborne particulate matter (PM10). Evolving Systems 11, 615–624 (2020). https://doi.org/10.1007/s12530-019-09263-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-019-09263-y

Keywords

Search

Navigation