Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations

Environmental Modelling and Software

Volumn 22, Issue 1, 2007, Pages 97-103

  Sousa, S I V ^a   Martins, F G ^a   Alvim Ferraz, M C M ^a   Pereira, M C ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

Artificial neural networks; Multiple linear regression; Principal components; Trophospheric ozone

Indexed keywords

CLIMATE CHANGE; COMPUTATIONAL COMPLEXITY; OZONE; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS;

DATA COLLINEARITY; MULTIPLE LINEAR REGRESSION; TROPHOSPHERIC OZONE;

FEEDFORWARD NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; OZONE; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; TROPOSPHERE;

EID: 33749247555     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2005.12.002     Document Type: Article

References (25)

1. Abdul-Wahab S.A., and Al-Alawi S.M. Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environmental Modelling & Software 17 (2002) 219-228
2. Abdul-Wahab S.A., Bakheit C.S., and Al-Alawi S.M. Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environmental Modelling & Software 20 (2005) 1263-1271
3. Aparício J.J.C., Jerónimo M.A.S., Martins F.G., Coelho M.A.N., Martins C., Braga A.S., and Costa C.A.V. Two different approaches for RDC modelling when simulating a solvent deasphalting plant. Computers & Chemical Engineering 26 (2002) 1369-1377
4. Borrego C., Tchepel O., Costa A.M., Amorim J.H., and Miranda A.I. Emission and dispersion modelling of Lisbon air quality at local scale. Atmospheric Environment 37 (2003) 5197-5205
5. Çamdevýren H., Demýr N., Kanik A., and Keskýn S. Use of principal component scores in multiple linear regression models for prediction of chlorophyll-a in reservoirs. Ecological Modelling 181 (2005) 581-589
6. Chaloulakou A., Saisana M., and Spyrellis N. Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Science of the Total Environment 313 (2003) 1-13
7. Cybenko G. Approximations by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 2 (1989) 303-314
8. Gardner M.W., and Dorling S.R. Statistical surface ozone models: an improved methodology to account for non-linear behaviour. Atmospheric Environment 34 (2000) 21-34
9. Gonçalves F.L.T., Carvalho L.M.V., Conde F.C., Latorre P.H.N., and Braga A.L.F. The effects of air pollution and meteorological parameters on respiratory morbidity during the summer in São Paulo City. Environment International 31 (2005) 343-349
10. Heo J.-S., and Kim D.-S. A new method of ozone forecasting using fuzzy expert and neural network systems. Science of the Total Environment 325 (2004) 221-237
11. Lengyel A., Héberger K., Paksy L., Bánhidi O., and Rajkó R. Prediction of ozone concentration in ambient air using multivariate methods. Chemosphere 57 (2004) 889-896
12. Martins F.G., and Coelho M.A.N. Application of feedforward artificial neural networks to improve process control of PID-based control algorithms. Computers & Chemical Engineering 24 (2000) 853-858
13. Mas J.F., Puig H., Palácio J.L., and Sosa-López A. Modelling deforestation using GIS and artificial neural networks. Environmental Modelling & Software 19 (2004) 461-471
14. Morabito F.C., and Versaci M. Fuzzy neural identification and forecasting techniques to process experimental urban air pollution data. Neural Networks 16 (2003) 493-506
15. 3. Environmental Monitoring & Assessment 101 (2005) 203-221
16. Peres-Neto P.R., Jackson D.A., and Somers K.M. How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Computational Statistics & Data Analysis 49 (2005) 974-997
17. San José R., Stohl A., Karatzas K., Bohler T., James P., and Pérez J.L. A modelling study of an extraordinary night time ozone episode over Madrid domain. Environmental Modelling & Software 20 (2005) 587-593
18. Schenker B., and Agarwal M. Cross-validated structure selection for neural networks. Computers & Chemical Engineering 20 (1996) 175-186
19. Schlink U., Dorling S., Pelikan E., Nunnari G., Cawley G., Junninen H., Greig A., Foxal R., Eben K., Chatterton T., Vondracek J., Richter M., Dostal M., Bertucco L., Kolehmainen M., and Doyle M. A rigorous inter-comparison of ground-level ozone predictions. Atmospheric Environment 37 (2003) 3237-3253
20. Schlink, U., Herbarth, O., Richter, M., Dorling, S., Nunnari, G., Cawley, G., Pelikan, E. Statistical models to assess the health effects and to forecast ground-level ozone. Environmental Modelling & Software, in press, doi:10.1016/j.envsoft.2004.12.002.
21. x and hydrocarbons in urban and polluted rural environments. Atmospheric Environment 33 (1999) 1821-1845
22. Slini, T., Kaprara, A., Karatzas, K., Moussiopoulos, N. PM10 forecasting for Thessaloniki, Greece. Environmental Modelling & Software, in press.
23. Thompson M.L., Reynolds J., Cox L.H., Guttorp P., and Sampson P.D. A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmospheric Environment 35 (2001) 617-630
24. Warne K., Prasad G., Rezvani S., and Maguire L. Statistical and computational intelligence techniques for inferential model development: a comparative evaluation and a novel proposition for fusion. Engineering Applications of Artificial Intelligence 17 (2004) 871-885
25. Watanabe K., Matsuura I., Abe M., Kubota M., and Himmelblau D.M. Incipient fault diagnosis of chemical processes via artificial neural networks. AIChE Journal 35 (1989) 1803-1811