Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability

Abstract

People give more importance concerning the overall quality of the modernized ecosystem. The pollution of air is one of the significant problems to be resolved as it restricted the ecological transformation of the modernized ecosystem. Therefore, it is fundamental to evaluate the implication of these ecological issues to enhance the urban ecosystem. This vital purpose of this research is to propose a canonical correlation analysis based hyper basis feedforward neural network classification (CCA-HBFNNC) model for evaluating sustainable urban environmental quality. The CCA-HBFNNC model initially acquires a large size of U.S. air pollution dataset as input. Then, a canonical correlative analysis based feature selection algorithm is applied in the CCA-HBFNNC model to select the key pollutant features, which bear fundamental implications to the modernize air pollution to maintain the level of urban sustainability. After the feature selection process, the CCA-HBFNNC model applies the HYPER BASIS FEEDFORWARD NEURAL NETWORK CLASSIFICATION (HBFNNC) algorithm in order to classify input air data based on chosen pollutants features. During the classification process, the HBFNNC algorithm used three critical layers namely hidden, output and input layers for efficiently categorizing each input data as higher or lower pollution level with higher accuracy. If the level of air pollution on the urban environment is higher, finally CCA-HBFNNC model significantly reduces the pollution level. In this way, the CCA-HBFNNC model attains improved urban sustainability levels when compared to sophisticated operation. An experimental evaluation of the CCA-HBFNNC model is determined in terms of CCA-HBFNNC model, time complexity and false-positive rate in consideration of the diversified number of air data retrieved from the big data sets. An investigational result shows that the proposed CCA-HBFNNC model can increases the sustainability level and minimizes the time complexity of urban development when contrasted with contemporary works.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. 1.

    Shao Q, Weng S-S, Liou JJH, Lo H-W, Jiang H (2019) Developing a sustainable urban-environmental quality evaluation system in China based on a hybrid model. Int J Environ Res Public Health 16(8):1–25

    Article  Google Scholar 

  2. 2.

    Wang Q, Dai H-N, Wang H (2017) A smart MCDM framework to evaluate the impact of air pollution on city sustainability: a case study from China. Sustainability 9(6):1–17

    Google Scholar 

  3. 3.

    Bibri SE (2019) On the sustainability of smart and smarter cities in the era of big data: an interdisciplinary and transdisciplinary literature review. J Big Data 6(25):1–64

    Google Scholar 

  4. 4.

    Angelidou M, Psaltoglou A, Komninos N, Kakderi C, Tsarchopoulos P, Panori A (2018) Enhancing sustainable urban development through smart city applications. J Sci Technol Policy Manag 9(2):146–169

    Article  Google Scholar 

  5. 5.

    Bibri SE (2018) The IoT for smart sustainable cities of the future: an analytical framework for sensor-based big data applications for environmental sustainability. Sustain Cities Soc Elsevier 38:230–253

    Article  Google Scholar 

  6. 6.

    Bibri SE (2018) Approaches to futures studies: a scholarly and planning approach to strategic smart sustainable city development. Smart sustainable cities of the future. Springer, Cham, pp 601–660

    Google Scholar 

  7. 7.

    Bibri SE, Krogstie J (2017) The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: a review and synthesis. J Big Data Springer 4:38

    Article  Google Scholar 

  8. 8.

    Sun Y, Du Y (2017) Big data and sustainable cities: applications of new and emerging forms of geospatial data in urban studies. Open Geospatial Data Softw Stand Springer 2(24):1–4

    Google Scholar 

  9. 9.

    Kharrazi A, Qin H, Zhang Y (2016) Urban big data and sustainable development goals: challenges and opportunities. Sustainability 8:1–6

    Google Scholar 

  10. 10.

    Qiao Y-K, Peng F-L, Sabri S, Rajabifard A (2019) Socio-environmental costs of underground space use for urban sustainability. Sustain Cities Soc Elsevier 51:101757

    Article  Google Scholar 

  11. 11.

    U.S. Air Pollution Dataset: https://data.world/data-society/us-air-pollution-data

  12. 12.

    Arulmurugan R, Anandakumar H (2018) Early detection of lung cancer using wavelet feature descriptor and feed forward back propagation neural networks classifier. In: Lecture notes in computational vision and biomechanics, pp 103–110. https://doi.org/10.1007/978-3-319-71767-8_9

  13. 13.

    Cuia Y, Zhang W, Bao H, Wang C, Cai W, Jian Yu, Streets DG (2019) Spatiotemporal dynamics of nitrogen dioxide pollution and urban development: satellite observations over China, 2005–2016. Resour Conserv Recycl Elsevier 142:59–68

    Article  Google Scholar 

  14. 14.

    Girardi P, Temporelli A (2017) Smartainability: a methodology for assessing the sustainability of the smart city. Energy Procedia Elsevier 111:810–816

    Article  Google Scholar 

  15. 15.

    Arulmurugan R, Anandakumar H (2018) Region-based seed point cell segmentation and detection for biomedical image analysis. Int J Biomed Eng Technol 27(4):273

    Article  Google Scholar 

  16. 16.

    Lu S, Liu Y (2018) Evaluation system for the sustainable development of urban transportation and ecological environment based on SVM. J Intell Fuzzy Syst 34(2):831–838

    MathSciNet  Article  Google Scholar 

  17. 17.

    Nick Hewitt C, Ashworth K, Rob MacKenzie A (2020) Using green infrastructure to improve urban air quality (GI4AQ). Ambio Springer 49(1):62–73

    Google Scholar 

  18. 18.

    del Mar Martínez-Bravo M, Martínez-del-Río J, Antolín-Lopez R (2019) Trade-offs among urban sustainability, pollution and livability in European cities. J Clean Prod Elsevier 224:651–660

    Article  Google Scholar 

  19. 19.

    Chang DL, Sabatini-Marques J, da Costa EM, Selig PM, Yigitcanlar T (2018) Knowledge-based, smart and sustainable cities: a provocation for a conceptual framework. J Open Innov Technol Market Complex Springer 4(5):1–17

    Google Scholar 

  20. 20.

    Tao Yu, Li F, Crittenden J, Lu Z, Ou W, Song Y (2019) Measuring urban environmental sustainability performance in China: a multi-scale comparison among different cities, urban clusters, and geographic regions. Cities Elsevier 94:200–210

    Article  Google Scholar 

  21. 21.

    Kadhim N, Mourshed M, Bray M (2016) Advances in remote sensing applications for urban sustainability. Euro-Mediterr J Environ Integr Springer 1(7):1–22

    Google Scholar 

  22. 22.

    Al-Nasrawi S, Adams C, El-Zaart A (2015) A conceptual multidimensional model for assessing smart sustainable cities. JISTEM J Inf Syst Technol Manag 12(3):541–558

    Google Scholar 

  23. 23.

    Jayasudha K, Kabadi MG (2020) Soft tissues deformation and removal simulation modelling for virtual surgery. Int J Intell Sustain Comput 1(1):83

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Anandakumar Haldorai.

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

Verify currency and authenticity via CrossMark

Cite this article

Haldorai, A., Ramu, A. Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability. Neural Process Lett (2020). https://doi.org/10.1007/s11063-020-10327-3

Download citation

Keywords

  • Air pollution
  • Big data
  • Canonical correlation analysis
  • Gaussian activation function
  • Hyper basis feedforward neural network
  • Pollutants features
  • Urban sustainability