Advertisement

Prediction Model of Urban Environmental Noise in Smart Environment

Chapter
  • 24 Downloads

Abstract

With the continuous progress of modern urbanization, urban noise pollution is also increasing. Noise pollution has been disturbing the normal life of people, and serious noise pollution may even affect people’s health. For this reason, it is necessary to make an effective noise prediction. Noise prediction uses historical data from noise monitoring points to predict future noise values, helping to provide effective noise regulation. In this chapter, the RF, BFGS, and GRU models are used to conduct feasibility studies for noise prediction. These three models are used to predict the public noise, traffic noise, and neighborhood noise. Through comprehensive comparative analysis of the experimental results, it can be concluded that the noise prediction performance of the BFGS model is the best in this experiment. Neighborhood noise is the most predictable among the three types of noise data.

References

  1. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology 100:270–278CrossRefGoogle Scholar
  2. Aimal, S., Javaid, N., Islam, T., Khan, W. Z., Aalsalem, M. Y., & Sajjad, H. (2019). An efficient CNN and KNN data analytics for electricity load forecasting in the smart grid. In Workshops of the International Conference on Advanced Information Networking and Applications (pp. 592–603). Springer.Google Scholar
  3. Anthopoulos, L., Janssen, M., & Weerakkody, V. (2019). A unified smart city model (USCM) for smart city conceptualization and benchmarking. in Smart cities and smart spaces: Concepts, methodologies, tools, and applications (pp. 247–264). IGI Global.Google Scholar
  4. Auger N, Duplaix M, Bilodeau-Bertrand M, Lo E, Smargiassi A (2018) Environmental noise pollution and risk of preeclampsia. Environmental Pollution 239:599–606CrossRefGoogle Scholar
  5. Badem H, Basturk A, Caliskan A, Yuksel ME (2018) A new hybrid optimization method combining artificial bee colony and limited-memory BFGS algorithms for efficient numerical optimization. Applied Soft Computing 70:826–844CrossRefGoogle Scholar
  6. Boggs PT, Byrd RH (2019) Adaptive, limited-memory BFGS algorithms for unconstrained optimization. SIAM Journal on Optimization 29(2):1282–1299MathSciNetCrossRefGoogle Scholar
  7. Borovykh A, Bohte S, Oosterlee CW (2019) Dilated convolutional neural networks for time series forecasting. Journal of Computational Finance, 22(4):73–101Google Scholar
  8. Chang D, Sun S, Zhang C (2019) An accelerated linearly convergent stochastic L-BFGS algorithm. IEEE transactions on neural networks and learning systems, 30(11):3338–3346Google Scholar
  9. Couronné R, Probst P, Boulesteix A-L (2018) Random forest versus logistic regression: A large-scale benchmark experiment. BMC Bioinformatics 19(1):270CrossRefGoogle Scholar
  10. Dewey RS, Hall DA, Guest H, Prendergast G, Plack CJ, Francis ST (2018) The physiological bases of hidden noise-induced hearing loss: Protocol for a functional neuroimaging study. JMIR Research Protocols 7(3):e79CrossRefGoogle Scholar
  11. Ge F, Ju Y, Qi Z, Lin Y (2018) Parameter estimation of a gaussian mixture model for wind power forecast error by Riemann l-bfgs optimization. IEEE Access 6:38892–38899CrossRefGoogle Scholar
  12. Grange SK, Carslaw DC, Lewis AC, Boleti E, Hueglin C (2018) Random forest meteorological normalisation models for Swiss PM 10 trend analysis. Atmospheric Chemistry Physics 18(9):6223–6239CrossRefGoogle Scholar
  13. Ishwaran H, Lu M (2019) Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival. Statistics in Medicine 38(4):558–582MathSciNetCrossRefGoogle Scholar
  14. Javaherian M, Abedi A, Khoeini F, Abedini Y, Asadi A, Ghanjkhanloo EJG (2018) Survey of noise pollution in Zanjan, and comparing them with standards. Journal of Applied Science 1(1):01–08Google Scholar
  15. Khan J, Ketzel M, Kakosimos K, Sørensen M, Jensen SS (2018) Road traffic air and noise pollution exposure assessment–A review of tools and techniques. Science of the Total Environment 634:661–676CrossRefGoogle Scholar
  16. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 2:1097–1105Google Scholar
  17. Kumar A, Kumar P, Mishra RK, Shukla A (2018) Study of air and noise pollution in mega cities of india. In: Environmental pollution. Springer, New York, pp 77–84CrossRefGoogle Scholar
  18. Liaw A, Wiener MJRN (2002) Classification and regression by randomForest. R News 2(3):18–22Google Scholar
  19. Liu H, Duan Z, Han F-Z, Li Y-F (2018) Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm. Energy Conversion Management 156:525–541CrossRefGoogle Scholar
  20. Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D et al (2016) Mllib: Machine Learning in Apache Spark 17(1):1235–1241Google Scholar
  21. Purwaningsih NMS, Alli MSA, Shams OU, Ghani JM, Ayyaturai S, Sailan AT et al (2018) Analysis of noise pollution: A case study of Malaysia’s university. Journal of International Dental 11(1):330–333Google Scholar
  22. Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) Xnor-net: Imagenet classification using binary convolutional neural networks. In: European conference on computer vision. Springer, Cham, pp 525–542Google Scholar
  23. Sengers F, Späth P, Raven R (2018) Smart city construction: Towards an analytical framework for smart urban living labs. In: Urban living labs. Routledge, New York, pp 74–88CrossRefGoogle Scholar
  24. Singh D, Kumari N, Sharma P (2018) A review of adverse effects of road traffic noise on human health. Fluctuation 17(1):1830001CrossRefGoogle Scholar
  25. Yang Z, Wang J (2018) A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm. Energy 160:87–100CrossRefGoogle Scholar
  26. Yuan Y-x JI (1991) A modified BFGS algorithm for unconstrained optimization. Numerical Analysis 11(3):325–332MathSciNetCrossRefGoogle Scholar
  27. Yuchi W, Gombojav E, Boldbaatar B, Galsuren J, Enkhmaa S, Beejin B et al (2019) Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city. Environmental Pollution 245:746–753CrossRefGoogle Scholar
  28. Zahid M, Ahmed F, Javaid N, Abbasi RA, Kazmi Z, Syeda H et al (2019) Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics 8(2):122CrossRefGoogle Scholar
  29. Zhu X, Du X, Kerich M, Lohoff FW, Momenan R (2018) Random forest based classification of alcohol dependence patients and healthy controls using resting state MRI. Neuroscience Letters 676:27–33CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. and Science Press 2020

Authors and Affiliations

  • Hui Liu
    • 1
  1. 1.School of Traffic and Transportation EngineeringCentral South UniversityChangshaChina

Personalised recommendations