Forecasting of daily total atmospheric ozone in Isfahan

Environmental Monitoring and Assessment

Volumn 157, Issue 1-4, 2009, Pages 235-241

  Yazdanpanah, H ^a   Karimi, M ^b   Hejazizadeh, Z ^c  

^a UNIVERSITY OF ISFAHAN   (Iran)

^b Iran Meteorological Organization   (Iran)

^c KHARAZMI UNIVERSITY   (Iran)

Author keywords

Forecasting; Meteorology; Neural network; Ozone

Indexed keywords

ARTIFICIAL NEURAL NETWORK MODELS; ATMOSPHERIC OZONE; DEW POINTS; GEOPOTENTIAL HEIGHT; INPUT MATRICES; NEURAL MODELS; NEURAL NETWORKS ALGORITHMS; OUTPUT MATRIX; STATION DATA; TOTAL COLUMN OZONE; TOTAL OZONE; WIND SPEED;

ATMOSPHERIC CHEMISTRY; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; METEOROLOGY; NEURAL NETWORKS; OZONE;

WEATHER FORECASTING;

OZONE;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC CHEMISTRY; ATMOSPHERIC MODELING; BACK PROPAGATION; DEW POINT; EXPERIMENTAL STUDY; FORECASTING METHOD; OZONE; PREDICTION; WIND DIRECTION; WIND VELOCITY;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; ATMOSPHERE; FORECASTING; IRAN; METEOROLOGICAL PHENOMENA; OZONE LAYER; PREDICTION; TEMPERATURE; VELOCITY;

AIR POLLUTANTS; AIR POLLUTION; ATMOSPHERE; CITIES; ENVIRONMENTAL MONITORING; FORECASTING; IRAN; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); OZONE;

ASIA; ESFAHAN [ESFAHAN (PRV)]; ESFAHAN [IRAN]; EURASIA; IRAN; MIDDLE EAST;

EID: 70349246907     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-008-0531-z     Document Type: Article

References (19)

1. Agirre-Basurko, E., Ibarra-Berastegi, G., & Madariaga, I. (2006). Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environmental Modelling & Software, 21(4), 430-446.
2. Aires, F., et al. (2002). A regularized neural net approach for retrieval of atmospheric and surface temperature with the IASI instrument. Journal of Applied Meteorology, 41, 144-159. doi: 10.1175/ 1520-0450(2002)041<0144:ARNNAF>2.0.CO;2.
3. Boznar, M., Lesjak, M., & Mlakar, P. (1993). A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmospheric Environment, 27B(2), 211-230.
4. Chen, C. (2000). A neuro-computing approach for modeling of residence time distribution (RTD) of carrot cubes in a vertical scraped surface heat exchanger (SSHE). Food Research International, 33(7), 549-556, August.
5. Dubrovsky, M. (1993). Robust locally weighted regression: Algorithm, programming and application to radiation data. Prague: Charles University.
6. Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multiplayer perceptron) - a review of applications in the atmospheric sciences. Atmospheric Environment, 32, 2627-2636. doi: 10.1016/S1352-2310(97)00447-0.
7. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359-366. doi: 10.1016/0893-6080(89)90020-8.
8. Jelte (2005). Depletion of stratospheric ozone over the Antarctic and Arctic: Responses of plants of polar terrestrial ecosystems to enhanced UV-B. Environmental Pollution, 137(3), 428-442, October.
9. Kalvova, J., & Halenka, T. (1995). On the relation between circulation and total ozone value extremes. Annales Geophysicae, 13, C616.
10. Lars, O. (2007). Stratospheric ozone, ultraviolet radiation, and cryptogams. Biological Conservation, 135(3), 326-333, March.
11. Lazaros, M. (2007). An artificial neural network model for mountainous water-resources management: The case of Cyprus mountainous watersheds. Environmental Modelling & Software, 22(7), 1066-1072, July.
12. Mubiru (2008). Predicting total solar irradiation values using artificial neural networks. Renewable Energy, 33(10), 2329-2332, Oct 2008.
13. Paschalidou, A. K., Iliadis, L. S., Kassomenos, P., & Bezirtzoglou, C. (2007). Neural modelling of the tropospheric ozone concentrations in an urban site. Proceedings of the 10th international conference on engineering applications of neural networks thessaloniki. Hellas, 29-31 Aug 2007.
14. Paul, S. (2000). A review of the observations and origins of the spring ozone maximum. Atmospheric Environment, 34(21), 3545-3561.
15. Sharda, R. (1994). Neural networks for the OR/MS analyst: An application bibliography. Interfaces, 24(2), 116-130.
16. Vahidinasab (2007). Day-ahead price forecasting in restructured power systems using artificial neural networks. Electric Power Systems Research, 78 (8), 1332-1342, Aug 2008.
17. Wasserman, P. D. (1989). Neural computing: Theory and practice (p. 210). New York, USA: Van Nostrand Reinhold.
18. Yazdanpanah, H. (2002). A Neural network model to predict wheat yield. Proceeding of map India conference, New delhi, 2002.
19. Zhang, X., & Cai, W.-G. (1997). Utilize ANN realize forecast modeling. Journal of Forecasting, 4, 120-135.