A multi-model approach to analysis of environmental phenomena

Environmental Modelling and Software

Volumn 22, Issue 5, 2007, Pages 674-682

  Giustolisi, O ^a   Doglioni, A ^a   Savic, D A ^b   Webb, B W ^b  

^a POLITECNICO DI BARI   (Italy)

^b UNIVERSITY OF EXETER   (United Kingdom)

Author keywords

Data reconstruction; Environmental modelling; Evolutionary computing; Evolutionary Polynomial Regression; Scientific knowledge discovery from data

Indexed keywords

COMPUTER SIMULATION; DATA PROCESSING; KNOWLEDGE ACQUISITION; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; POLYNOMIALS; REGRESSION ANALYSIS;

DATA RECONSTRUCTION; ENVIRONMENTAL MODELLING; EVOLUTIONARY COMPUTING; EVOLUTIONARY POLYNOMIAL REGRESSION (EPR);

ENVIRONMENTAL ENGINEERING;

ENVIRONMENTAL MODELING; STREAM; THERMAL PHENOMENA;

EID: 33845689247     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2005.12.026     Document Type: Article

References (24)

1. Babovic V., and Keijzer M. Genetic programming as a model induction engine. Journal of Hydroinformatics, IAHR-IWA 2 1 (2000) 35-61
2. Bennis S., Berrada F., and Kang N. Improving single-variable and multivariable techniques for estimating missing hydrological data. Journal of Hydrology 191 (1997) 87-105
3. Davidson J.W., Savic D.A., and Walters G.A. Symbolic and numerical regression: a hybrid technique for polynomial approximators. In: John R., and Birkenhead R. (Eds). Advances in Soft Computing: Soft Computing Techniques and Applications (1999), Physica-Verlag, Heidelberg
4. Davidson J.W., Savic D.A., and Walters G.A. Symbolic and numerical regression: experiments and applications. Information Sciences 150 1/2 (2003) 95-117
5. Draper N.R., and Smith H. Applied Regression Analysis (1998), John Wiley and Sons, New York
6. Efron B. Bootstrap methods. Another look at the jackknife. Annals of Statistics 7 (1979) 1-26
7. EPR. Available at: (2004).
8. Giustolisi O. Using genetic programming to determine Chèzy resistance coefficient in corrugated channels. Journal of Hydroinformatics, IAHR-IWA 6 3 (2004) 157-173
9. Giustolisi, O., Savic, D.A., 2003. Evolutionary Polynomial Regression (EPR): Development and Applications. Report 2003/01. School of Engineering, Computer Science and Mathematics, Centre for Water Systems, University of Exeter.
10. Giustolisi, O., Savic, D.A., 2004a. Decision Support for Water Distribution System Rehabilitation Using Evolutionary Computing. Proceedings from the ACTUI Seminar, Exeter, pp. 76-83.
11. Giustolisi, O., Savic, D.A., 2004b. A novel strategy to perform genetic programming: Evolutionary Polynomial Regression. Sixth International Conference on Hydroinformatics, Singapore, vol. 1, pp. 787-794.
12. Giustolisi, O., Savic, D.A., 2006. A symbolic data-driven technique based on evolutionary polynomial regression. Journal of Hydroinformatics, IAHR-IWA, 8 (3).
13. Giustolisi, O., Savic, D.A., Doglioni, A., 2004a. Data Reconstruction and Forecasting by Evolutionary Polynomial Regression. Sixth International Conference on Hydroinformatics, Singapore, vol. 2, pp. 1245-1252.
14. Giustolisi, O., Savic, D.A., Doglioni, A., Laucelli, D., 2004b. Knowledge Discovery by Evolutionary Polynomial Regression. Sixth International Conference on Hydroinformatics, Singapore, vol. 2, pp. 1647-1654.
15. Giustolisi, O., Doglioni, A., Savic, D.A., di Pierro, F., 2004c. A new multiobjective strategy to support model selection for environmental modelling. Fourth International Workshop on Environmental Applications of Machine Learning (EAML), Bled, Slovenia.
16. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison and Wesley, Reading, MA
17. Haykin S. Neural Networks: A Comprehensive Foundation 2/e (1999), Prentice-Hall Inc., Upper Saddle River, New Jersey
18. Lawson C.L., and Hanson R.J. Solving Least Squares Problems (1974), Prentice-Hall (Chapter 23), p. 161
19. Ljung L. System Identification: Theory for the User 2e (1999), Prentice-Hall Inc., Upper Saddle River, New Jersey
20. McKay, B., Willis, M.J., Barton, G.W., 1995. Using a tree structured genetic algorithm to perform symbolic regression. Genetic Algorithms in Engineering Systems: Innovations and Applications, IEE conference publication no. 414, pp. 487-492.
21. Minns A.W. Subsymbolic methods for data mining in hydraulic engineering. Journal of Hydroinformatics, IAHR-IWA 2 1 (2000) 3-13
22. Mohseni O., and Stefan H.G. Stream temperature/air temperature relationship: a physical interpretation. Journal of Hydrology 218 (1999) 128-141 (Elsevier)
23. Watson, A., Parmee, I., 1996. Systems Identification Using Genetic Programming, in Proceedings of ACEDC '96, PEDC, University of Plymouth, UK.
24. Webb B.W., Clack P.D., and Walling D.E. Water-air temperature relationships in a Devon river system and the role of flow. Hydrological Processes 17 15 (2003) 3069-3084