Abstract
The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser–Meyer–Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott’s indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.
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Abdul-Wahab SA, Al-Alawi SM (2002) Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environ Model Softw 17:219–228
Abdul-Wahab SA, Bakheit CS, Al-Alawi SM (2005) Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ Model Softw 20(10):1263–1271
Acharya N, Kar SC, Kulkarni MA, Mohanty UC, Sahoo LN (2011) Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India. J Earth Syst Sci 120:795–805
Acharya N, Chattopadhyay S, Kulkarni MA, Mohanty UC (2012) A neurocomputing approach to predict monsoon rainfall in monthly scale using sst anomaly as a predictor. Acta Geophysica 60:260–279
Alexander G, Chatterjee K (1980) Atmospheric ozone measurements in India. Proc Indian Natl Sci Acad 46:234–244
Alexandris D, Varotsos C, Kondratyev KY, Chronopoulos G (1999) On the altitude dependence of solar effective UV. Phys Chem Earth C Sol Terr Planet Sci 24:515–517
Baertsch-Ritter N, Keller J, Dommen J, Prevot AS (2004) Effects of various meteorological conditions and spatial emission reductions on the ozone concentration and ROG/NOx limitation in the Milan area (I). Atmos Chem Phys 4:423–438
Bandyopadhyay G, Chattopadhyay S (2007) Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. Int J Environ Sci Technol 4:141–149
Bekki S, Toumi R, Pyle JA (1993) Role of sulphur photochemistry in tropical ozone changes after the eruption of Mount Pinatubo. Nature 362:331–333
Bilgili M (2010) Prediction of soil temperature using regression and artificial neural network models. Meteorol Atmos Phys 110:59–70
Bonasoni P, Cristofanelli P, Calzolari F, Bonaf U, Evangelisti F, Stohl A, Zauli SS, van Dingenen R, Colombo T, Balkanski Y (2004) Aerosol-ozone correlation during dust transport episodes. Atmos Chem Phys 4:1201–1215
Burrows W, Benjamin M, Beauchamp S, Lord E, McCollor D, Thomson B (1995) CART decision-tree statistical analysis and prediction of summer season maximum surface ozone for the Vancouver, Montreal, and Atlantic regions of Canada. J Appl Meteorol 34:1848–1862
Chakrabarty DK, Peshin SK, Pandya KV, Shah NC (1998) Long-term trend of ozone column over the Indian region. J Geophys Res 103(D15):19245–19251. doi:10.1029/98JD00818
Chaloulakoua A, Saisanaa M, Spyrellisa N (2003) Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Sci Total Environ 313:1–13
Chattopadhyay G, Chattopadhyay S (2009a) Dealing with the complexity of earthquake using neurocomputing techniques and estimating its magnitudes with some low correlated predictors. Arab J Geosci 2:247–255
Chattopadhyay G, Chattopadhyay S (2009b) Predicting daily total ozone over Kolkata, India: skill assessment of different neural network models. Meteorol Appl 16:179–190
Chattopadhyay G, Chattopadhyay S (2009c) Autoregressive forecast of monthly total ozone concentration: a neurocomputing approach. Comput Geosci 35:1925–1932
Chaturvedi DK, Mohan M, Singh RK, Kalra PK (2003) Improved generalized neuron model for short-term load forecasting. Soft Comput 8:10–18. doi:10.1007/s00500-002-0241-3
Cracknell AP, Varotsos CA (1994) Ozone depletion over Scotland as derived from nimbus-7 toms measurements. Int J Remote Sens 15:2659–2668
Cracknell AP, Varotsos CA (1995) The present status of the total ozone depletion over Greece and Scotland—a comparison between Mediterranean and more northerly latitudes. Int J Remote Sens 16:1751–1763
Dave JV (1978) Effect of aerosols on the estimation of total ozone in an atmospheric column from the measurements of its ultraviolet radiance. J Atmos Sci 35:899–911
Dawson JP, Adam PJ, Pandis SN (2007) Sensitivity of ozone to summertime climate in the eastern USA: a modeling case study. Atmos Environ 41:1494–1511
De SS, Chattopadhyay G, Bandyopadhyay B, Paul S (2011) A neurocomputing approach to the forecasting of monthly maximum temperature over Kolkata, India using total ozone concentration as predictor. Comptes Rendus Geosci 343:664–676
Eder BK, LeDuc SK, Sickles JE III (1999) A climatology of total ozone mapping spectrometer data using rotated principal component analysis. J Geophys Res 104(D3):3691–3709
Firat M, Güngör M (2009) Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piers. Adv Eng Softw 40:731–737
Fusco, Andrew C, Salby ML (1999) Interannual variations of total ozone and their relationship to variations of planetary wave activity. J Clim 12:1619–1629
Gardner M, Dorling S (1998) Artificial neural network (the multilayer per-ceptron)—a review of applications in the atmospheric sciences. Atmos Environ 6:2627–2636
Glandorf M, Arola A, Bais A, Seckmeyer G (2005) Possibilities to detect trends in spectral UV irradiance. Theor Appl Climatol 81:33–44
Goncalves FLT, Carvalho LMV, Conde FC, Latorre PHN, Braga ALF (2005) The effects of air pollution and meteorological parameters on respiratory morbidity during the summer in Sao Paulo City. Environ Int 31:343–349
Hsu NC, McPeters RD, Seftor CJ, Thompson AM (1997) Effect of an improved cloud climatology on the total ozone mapping spectrometer total ozone retrieval. J Geophys Res 102:4247–4255
Hubbard M, Cobourn G (1998) Development of a regression model to forecast ground-level ozone concentration in Louisville KY. Atmos Environ 32:2637–2647
Jiang F, Wang T, Wang T, Xie M, Zhao H (2008) Numerical modeling of a continuous photochemical pollution episode in Hong Kong using WRF-chem. Atmos Environ 42:8717–8727
Jolliffe IT (2002) Principal component analysis. Springer, New York
Katsambas A, Varotsos CA, Veziryianni GA, Antoniou C (1997) Surface solar ultraviolet radiation: a theoretical approach of the SUVR reaching the ground in Athens, Greece. Environ Sci Pollut Res 4:69–73
Kelly NA, Ferman MA, Wolff GT (1986) The chemical and meteorological conditions associated with high and low ozone concentrations in southeastern Michigan and nearby areas of Ontario. J Air Pollut Control Assoc 36:150–158
Kondratyev KY, Varotsos CA (1996) Global total ozone dynamics—impact on surface solar ultraviolet radiation variability and ecosystems. Environ Sci Pollut Res 3:205–209
Korsog PE, Wolff GT (1991) The examination of urban ozone trends in the northeastern US (1973–1983) using a robust statistical method. Atmos Environ 25B:47–57
Kovac-Andric E, Brana J, Gvozdic V (2009) Impact of meteorological factors on ozone concentrations modelled by time series analysis and multivariate statistical methods. Ecol Informat 4(2):117–122
Liu CM, Liu SC, Shen SH (1990) A study of Taipei ozone problem. Atmos Environ 24A:1461–1478
London J (1980) In proceedings of the NATO advanced institute on atmospheric ozone (Portugal). FAA Rep. FAA-EE-80–20, Washington
Makra L, Mika J, Bartzokas A, Béczi R, Borsos E, Sümeghy Z (2006) An objective classification system of air mass types for Szeged, Hungary, with special interest in air pollution levels. Meteorol Atmos Phys 92:115–137
Maqsood I, Khan MR, Huang GH, Abdalla R (2005) Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada. Eng Appl Artif Intell 18:115–125
McKendry IG (1993) Ground-level ozone in Montreal, Canada. Atmos Environ 27B:93–103
Moussiopoulos N, Sahm P, Kessler C (1995) Numerical simulation of photochemical smog formation in Athens, Greece—a case study. Atmos Environ 29:3619–3632
NASA (1998) Earth Probe Total Ozone Mapping Spectrometer (TOMS) Data Products User’s Guide, Goddard Space Flight Center: Greenbelt, Maryland 20771
Nunnari G, Nucifiora AFM, Radineri C (1998) The application of neural techniques to the modelling of time series of atmospheric pollution data. Ecol Model 111:187–205. doi:10.1016/S0304-3800(98)00118-5
Othman A, Owen L (2001) The multidimensionality of CARTER model to measure customer Service Quality (SQ) in Islamic banking industry: a study in Kuwait Finance House. Int J Islamic Finan Serv 3:1–26
Patil SD, Revadekar JV (2009) Extremes in total ozone content over northern India. Int J Remote Sens 30:2389–2397
Perez P, Reyes J (2001) Prediction of particulate air pollution using neural techniques. Neural Comp Appl 10:165–171
Perez P, Trier A, Reyes J (2000) Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile. Atmospheric Environ 34:1189–1196
Poulin L, Evans WFJ (1994) Metoz: total ozone from meteorological parameters. Atmosphere-Ocean 32:285–297
Principe JC, Rathie A, Kuo JM (1992) Prediction of chaotic time series with neural networks and the issue of dynamic modeling. Int J Bifurcat Chaos 2:989–996. doi:10.1142/S0218127492000598
Prybutok R, Junsub Y, Mitchell D (2000) Comparison of neural network models with ARIMA and regression models for prediction of Houston’s daily maximum ozone concentrations. Euro J Operat Res 122:31–40. doi:10.1016/S0377-2217(99)00069-7
Rabbe A, Larsen SHH (1997) Two examples of how the total ozone is influenced by the upper air circulation pattern. J Atmos Sol Terr Phys 59:753–759
Robeson S, Steyn D (1990) Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations. Atmos Environ 24:303–312
Ryan W (1995) For ecasting severe ozone episodes in the Baltimore metropolitan area. Atmos Environ 29:2387–2398
Sahoo A, Sarkar S, Singh RP, Kafatos M, Summers ME (2005) Declining trend of total ozone column over the northern parts of India. Int J Remote Sens 26:3433–3440
Salazar-Ruiz E, Ordieres JB, Vergara EP, Capuz-Rizo SF (2008) Development and comparative analysis of tropospheric ozone prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US). J Environ Model Softw 23:1056–1069
Seinfeld J (1988) Ozone air quality models—a critical review. J Air Pollut Control Assoc 38:616–645
Singh RP, Sarkar S, Singh A (2002) Effect of El Niño on inter-annual variability of ozone during the period 1978–2000 over the Indian sub-continent and China. Int J Remote Sens 23:2449–2456
Sousa SIV, Martins FG, Alvim-Ferraz MCM, Pereira MC (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ Model Softw 22:97–103
Tiwari VS (1973) Ozone distribution over India. Pure Appl Geophys 106:1010–1017
Varotsos C (2002) The southern hemisphere ozone hole split in 2002. Environ Sci Pollut Res 9:375–376
Varotsos CA, Cracknell AP (1994) Three years of total ozone measurements over Athens obtained using the remote sensing technique of a Dobson spectrophotometer. Int J Remote Sens 15:1519–1524
Varotsos C, Kalabokas P, Chronopoulos G (1994) Association of the laminated vertical ozone structure with the lower-stratospheric circulation. J Appl Meteorol 33:473–476
Varotsos CA, Chronopoulos GJ, Katsikis S, Sakellariou NK (1995) Further evidence of the role of air-pollution on solar ultraviolet-radiation reaching the ground. Int J Remote Sens 16:1883–1886
Varotsos CA, Efstathiou MN, Kondratyev KY (2003) Long-term variation in surface ozone and its precursors in Athens, Greece—a forecasting tool. Environ Sci Pollut Res 10:19–23
Varotsos C, Ondov J, Efstathiou M (2005) Scaling properties of airpollution in Athens, Greece and Baltimore, Maryland. Atmos Environ 39:4041–4047
Vaughan G, Price JD (1991) On the relation between total ozone and meteorology. Q J R Meteorol Soc 117:1281–1291
Viotti P, Liuti G, Di Genova P (2002) Atmospheric urban pollution: application of an artificial neural network to the city of Perugia. Ecol Model 148:27–46. doi:10.1016/S0304-3800(01)00434-3
Wilks DS (2006) Statistical methods in atmospheric sciences, 2nd edn. Elsevier, Oxford
Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63:1309–1313
Xu J, Zhu Y (1994) Some characteristics of ozone concentrations and their relations with meteorological factors in Shanghai. Atmos Environ 28:3387–3392
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Chattopadhyay, G., Chattopadhyay, S. & Chakraborthy, P. Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India. Theor Appl Climatol 109, 221–231 (2012). https://doi.org/10.1007/s00704-011-0569-7
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DOI: https://doi.org/10.1007/s00704-011-0569-7