A CASE STUDY OF ENVIRONMENTAL MONITORING DATA ANALYSIS AND FORECASTING MODEL

Wei Zhou, Xiaoyu Chen, and Binyue Cui

References

  1. [1] G. Corani, Air quality prediction in Milan: Feed-forward neuralnetworks, pruned neural networks and lazy learning, EcologicalModelling, 185(2), 2005, 513–529.
  2. [2] S. Li, S. Batterman, E. Wasilevich, R. Wahl, et al., Associationof daily asthma emergency department visits and hospitaladmissions with ambient air pollutants among the pediatricMedicaid population in Detroit: Time-series and time-stratifiedcase-crossover analyses with threshold effects, EnvironmentalResearch, 111(8), 2011, 1137–1147.
  3. [3] K. Bhaskaran, S. Hajat, B. Armstrong, A. Haines, et al., Theeffects of hourly differences in air pollution on the risk ofmyocardial infarction: Case crossover analysis of the MINAPdatabase. BMJ (Clinical Research ed.), 343, 2011, d5531.
  4. [4] B.K. Butland, B. Armstrong, R.W. Atkinson, P. Wilkinson,et al., Measurement error in time-series analysis: A simulationstudy comparing modelled and monitored data, BMC MedicalResearch Methodology, 13, 2013, 136.
  5. [5] A. Zanobetti, F. Dominico, Y. Wang, and J.D. Schwartz,A national case-crossover analysis of the short-term effectof PM2.5 of hospitalizations and mortality in subjects withdiabetes and neurological disorders, Environmental Health, 13,2014, 38.
  6. [6] S.M. Al-Alawi, S.A. Abdul-Wahab, and C.S. Bakheit, Combining principal component regression and artificial neuralnetworks for more accurate predictions of ground-level ozone,Environmental Modelling & Software, 23(4), 2008, 396–403.
  7. [7] J.C.M. Pires, F.G. Martins, S.I.V. Sousa, M.C.M. Alvim-Ferraz, et al., Prediction of the daily mean PM10 concentrations using linear models, American Journal of EnvironmentalSciences, 4(5), 2008, 445–453.
  8. [8] M. Kumar and D.P. Garg, Intelligent sensor uncertainty modelling techniques and data fusion, Control and Intelligent Systems, 37(2), 2009, 67–77.
  9. [9] M. Cai, Y. Yin, and M. Xie, Prediction of hourly air pollutant concentrations near urban arterials using artificial neuralnetwork approach, Transportation Research Part D: Transportand Environment, 14(1), 2009, 32–41.
  10. [10] P. Perez and J. Reyes, Prediction of maximum of 24h averageof PM10 concentrations 30h in advance in Santiago, Chile,Atmospheric Environment, 36(28), 2002, 4555–4561.
  11. [11] A.K. Shah and D.M. Adhyaru, HJB solution-based optimalcontrol of hybrid dynamical systems using multiple linearizedmodel, Control & Intelligent Systems, 44(2), 2016, 52–58.
  12. [12] E. Stadlober, S. Hormann, and B. Pfeiler, Quality and performance of a PM10 daily forecasting model, AtmosphericEnvironment, 42, 2008, 1098–1109.
  13. [13] A. Vlachogianni, P. Kassomenos, A. Karppinen, S. Karakitsios,et al., Evaluation of a multiple regression model for theforecasting of the concentrations of NOX and PM10 in Athensand Helsinki, Science of the Total Environment, 409(8), 2011,1559–1571.
  14. [14] A. Afzali, M. Rashid, B. Sabariah, and M. Ramli, PM10pollution: Its prediction and meteorological influence in Pasir-Gudang, Johor, IOP Conf. Ser.: Earth Environ. Sci., 18(1),2014, 012100.
  15. [15] A.Z. Ul-Saufie, A.S. Yahya, and N.A. Ramli, Improving multiple linear regression model using principal component analysisfor predicting PM10 concentration in Seberang Perai, PulauPinang, International Journal of Environmental Sciences, 2(2),2011, 403–408.
  16. [16] N. Otero, J. Sillmann, J.L. Schnell, H.W. Rust, et al., Synopticand meteorological drivers of extreme ozone concentrationsover Europe, Environmental Research Letters, 11(2), 2016,024005.

Important Links:

Go Back