Environmental Modelling through Chaotic Approach: A Case Study on Ozone and Temperature Time Series

  • Authors

    • Nor Zila Abd Hamid
    • . .
  • Modelling, Chaotic Approach, Ozone, Temperature, Prediction
  • The main objective of this study is to develop a suitable model for analysing and predicting environmental variable. Since O3 pollution is harmful and the changes in temperature can have serious consequences to health, therefore, both variables are chosen. Tanjong Malim, a semi-urban educational area located in Perak Malaysia is selected since it is well known that educational area is frequently visited by peoples. Thus, the environmental modelling is necessary. Analysis by the phase space plot and Cao method shown that the chaotic nature presents in both observed time series. Hence, both time series are predicted through the chaotic approach. Results from the local mean approximation method shown that both time series are predicted well with correlation coefficient near to one. Therefore, chaotic approach is suitable to be applied in environmental modelling. These findings are expected to help stakeholders such as Ministry of Education, Meteorological Department and Department of Environment in having a better environment management.



  • References

    1. [1] Peng, Z., Liu, C., Xu, B., Kan, H., Wang, W., Long-term exposure to ambient air pollution and mortality in a Chinese tuberculosis cohort. Sci. Total Environ., 2017. 580: p. 1483–1488.

      [2] Cakmak, S., Hebbern, C., Vanos, J. Crouse, D.L., Burnett, R., Ozone exposure and cardiovascular-related mortality in the Canadian Census Health and Environment Cohort (CANCHEC) by spatial synoptic classification zone. Environ. Pollut., 2016. 214(2): p. 589–599.

      [3] Maji, S., Ahmed, S., Siddiqui, W.A., Ghosh, S., Short term effects of criteria air pollutants on daily mortality in Delhi, India. Atmos. Environ., 2017. 150: p. 210–219.

      [4] Ghorbani, M.A., Daneshfaraz, R., Arvanagi, H., Pourzangbar, A., Mahdi, S., Kar, S.K., Local prediction in river discharge time series. J. Civ. Eng. Urban., 2012. 2(2): p. 51–55.

      [5] Domenico, M.D., Ali, M., Makarynskyy, O., Makarynska, D., Chaos and reproduction in sea level. Appl. Math. Model., 2013. 37(6): p. 3687–3697.

      [6] Chelani, A.B., Devotta, S., Nonlinear analysis and prediction of coarse particulate matter concentration in ambient air. J. Air Waste Manag. Assoc., 2006. 56: p. 78–84.

      [7] Indira, P., Inbanathan, S.S.R., Selvaraj, R.S., Suresh, A.A., Chaotic analysis on surface ozone measurements at tropical urban coastal station Chennai, India. Int. J. Earth Sci., 2016. 2(1): p. 1–8.

      [8] Adenan, N.H., Hamid, N.Z.A., Mohamed, Z., Noorani, M.S.M., A pilot study of river flow prediction in urban area based on phase space reconstruction. AIP Conference Proceedings, 2017. 1870.

      [9] Hamid, N.Z.A., Noorani, M.S.M., Adenan, N.H., Chaotic analysis and short-term prediction of ozone pollution in Malaysian urban area. J. Phys. Conf. Ser., 2017. 890(1): p. 1-5.

      [10] Zaim, W.N.A.W.M., Hamid, N.Z.A., Forecasting ozone pollutant (O3) in Universiti Pendidikan Sultan Idris, Tanjung Malim, Perak, Malaysia based on monsoon using chaotic approach. Sains Malaysiana, 2017. 46(12): p. 2523–2528.

      [11] Hamid, N.Z.A. Noorani, M.S.M., New improved chaotic approach model application on forecasting ozone concentration time series. Sains Malaysiana, 2017. 46(8): p. 1333-1339.

      [12] Hamid, N.Z.A., Noorani, M.S.M., A pilot study using chaotic approach to determine characteristics and forecasting of PM10 concentration time series. Sains Malaysiana, 2014. 43(3): p. 475–481.

      [13] Chen, J., Islam, S., Biswas, P., Nonlinear dynamics of hourly ozone concentrations: nonparametric short term prediction. Atmos. Environ., 1998. 32(11): p. 1839–1848.

      [14] Cao, L., Practical method for determining the minimum embedding dimension of a scalar time series, Phys. D, 1997. 110: p. 43–50

      [15] Jayawardena, A.W., Runoff forecasting using a local approximation method. IAHS, 1997, 239: p. 167–171.

      [16] Hamid, N.Z.A., Noorani, M.S.M., Modeling of prediction system: An application of the nearest neighbor approach to chaotic data. App. Math. Comp. Intel., 2013. 2(1): p. 137–148.

      [17] Sivakumar, B., A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. J. Hydrol., 2002. 258: p. 149–162.

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    Abd Hamid, N. Z., & ., . (2018). Environmental Modelling through Chaotic Approach: A Case Study on Ozone and Temperature Time Series. International Journal of Engineering & Technology, 7(3.7), 590-593. https://doi.org/10.14419/ijet.v7i3.7.19057