MLP based predictive model for surface ozone concentration over an urban area in the Gangetic West Bengal during pre-monsoon season

Journal of Atmospheric and Solar-Terrestrial Physics

Volumn 184, Issue , 2019, Pages 57-62

  Chattopadhyay, Goutami ^a   Midya, Subrata Kumar ^a   Chattopadhyay, Surajit ^b  

^a UNIVERSITY OF CALCUTTA   (India)

^b AMITY UNIVERSITY   (India)

Author keywords

Gangetic West Bengal; Gradient descent; Multilayer perceptron; Prediction; Surface ozone

Indexed keywords

ATMOSPHERIC THERMODYNAMICS; EFFICIENCY; FORECASTING; MULTILAYER NEURAL NETWORKS; MULTILAYERS;

ACTIVATION FUNCTIONS; GRADIENT DESCENT; MULTI LAYER PERCEPTRON; NON-LINEAR REGRESSION; SIGMOID NONLINEARITY; SURFACE OZONE; SURFACE OZONE CONCENTRATIONS; WEST BENGAL;

OZONE;

EID: 85060860398     PISSN: 13646826     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jastp.2019.01.008     Document Type: Article

References (32)

1. Ahmed, K., Shahid, S., Haroon, S.B., Xiao-Jun, W., Multilayer perceptron neural network for downscaling rainfall in arid region: a case study of Baluchistan, Pakistan. J. Earth Syst. Sci. 124:6 (2015), 1325–1341.
2. Bonasoni, P., Cristofanelli, P., Calzolari, F., Bonafe, U., Evangelisti, F., Stohl, A., Zauli Sajani, S., Dingenen, R.V., Colombo, T., Balkanski, Y., Aerosol-ozone correlations during dust transport episodes. Atmos. Chem. Phys. 4:5 (2004), 1201–1215.
3. Chandrasekar, A., Basics of atmospheric science. 2010, PHI Learning Pvt. Ltd.
4. Chattopadhyay, S., Chattopadhyay, G., Comparative study among different neural net learning algorithms applied to rainfall time series. Meteorol. Appl.: J. Forecast. Practical Appl. Train. Tech. Model. 15:2 (2008), 273–280.
5. Chattopadhyay, S., Chattopadhyay, G., Gradient descent learned ANN for Indian summer monsoon rainfall and efficiency assessment through Shannon-Fano coding. J. Atmos. Sol. Terr. Phys. 179 (2018), 202–205.
6. Clausen, P.A., Wilkins, C.K., Wolkoff, P., Nielsen, G.D., Chemical and biological evaluation of a reaction mixture of R-(+)-limonene/ozone: formation of strong airway irritants. Environ. Int. 26:7–8 (2001), 511–522.
7. Colbeck, I., Harrison, R.M., Ozone—secondary aerosol—visibility relationships in North-West England. Sci. Total Environ. 34:1–2 (1984), 87–100.
8. Fiore, A.M., Jacob, D.J., Field, B.D., Streets, D.G., Fernandes, S.D., Jang, C., Linking ozone pollution and climate change: The case for controlling methane. Geophysical Research Letters, 29(19), 2002 25-1.
9. Gardner, M.W., Dorling, S.R., Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32:14–15 (1998), 2627–2636.
10. Geddes, J.A., Murphy, J.G., Wang, D.K., Long term changes in nitrogen oxides and volatile organic compounds in Toronto and the challenges facing local ozone control. Atmospheric Environment 43:21 (2009), 3407–3415.
11. Grivas, G., Chaloulakou, A., Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmos. Environ. 40:7 (2006), 1216–1229.
12. Hertel, О., Berkowicz, R., Vignati, E., Palmgren, F., Urban air pollution of nitrogen oxides from traffic. Paper in proceedings, 1995.
13. Hinton, G.E., Salakhutdinov, R.R., Reducing the dimensionality of data with neural networks. Science 313:5786 (2006), 504–507.
14. Jana, P.K., Sarkar, D., Saha, D.K., Midya, S.K., Effect of cloud occurrences on tropospheric ozone over, Alipore (22.52° N, 88.33° E), India. J. Earth Syst. Sci. 121:3 (2012), 711–722.
15. Jenkin, M.E., Clemitshaw, K.C., Ozone and other secondary photochemical pollutants: chemical processes governing their formation in the planetary boundary layer. Atmos. Environ. 34:16 (2000), 2499–2527.
16. Jungwirth, P., Rosenfeld, D., Buch, V., A possible new molecular mechanism of thundercloud electrification. Atmos. Res. 76:1–4 (2005), 190–205.
17. Kalogirou, S.A., Artificial neural networks in renewable energy systems applications: a review. Renew. Sustain. Energy Rev. 5:4 (2001), 373–401.
18. Karlik, B., Olgac, A.V., Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int. J. Artif. Intell. Expet. Syst. 1:4 (2011), 111–122.
19. Kondratyev, K.Y., Varotsos, C.A., Global total ozone dynamics. Environ. Sci. Pollut. Res. 3:3 (1996), 153–157.
20. Kovač-Andrić E., Sheta, A., Faris, H., Šrajer Gajdošik, M., Forecasting ozone concentrations in the east of Croatia using nonparametric Neural Network Models. J. Earth Syst. Sci. 125:5 (2016), 997–1006.
21. Midya, S.K., Sarkar, H., Saha, U., Sharp depletion of atmospheric refractive index associated with Nor'wester over Gangetic West Bengal: a possible method of forecasting Nor'wester. Meteorol. Atmos. Phys. 111:3–4 (2011), 149–152.
22. Mohanakumar, K., Stratosphere Troposphere Interactions: an Introduction. 2008, Springer Science & Business Media.
23. Moustris, K.P., Larissi, I.K., Nastos, P.T., Koukouletsos, K.V., Paliatsos, A.G., Development and application of artificial neural network modeling in forecasting PM 10 levels in a Mediterranean city. Water, Air, Soil Pollut., 224(8), 2013, 1634.
24. Paoletti, E., De Marco, A., Beddows, D.C., Harrison, R.M., Manning, W.J., Ozone levels in European and USA cities are increasing more than at rural sites, while peak values are decreasing. Environmental Pollution 192 (2014), 295–299.
25. Rakov, V.A., Uman, M.A., Lightning: Physics and Effects. 2003, Cambridge University Press.
26. Ravishankara, A.R., Daniel, J.S., Portmann, R.W., Nitrous oxide (N2O): the dominant ozone-depleting substance emitted in the 21st century. Science 326:5949 (2009), 123–125.
27. Rojas, R., Neural Networks: a Systematic Introduction. 2013, Springer Science & Business Media.
28. Seinfeld, J.H., Pandis, S.N., Atmospheric Chemistry and Physics: from Air Pollution to Climate Change. 2012, John Wiley & Sons.
29. Varotsos, C., The southern hemisphere ozone hole split in 2002. Environ. Sci. Pollut. Res. 9:6 (2002), 375–376.
30. Willmott, C.J., Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 63 (1982), 1309–1313.
31. Yegnanarayana, B., Artificial Neural Networks. 2009, PHI Learning Pvt. Ltd.
32. Zhang, B.N., Oanh, N.K., Photochemical smog pollution in the Bangkok Metropolitan Region of Thailand in relation to O3 precursor concentrations and meteorological conditions. Atmospheric Environment 36:26 (2002), 4211–4222.