Testing the structure of a hydrological model using Genetic Programming

Journal of Hydrology

Volumn 397, Issue 1-2, 2011, Pages 1-9

  Selle, Benny ^a   Muttil, Nitin ^b  

^a DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

^b VICTORIA UNIVERSITY   (Australia)

Author keywords

Data mining; Data based modelling; Diagnostic model evaluation; Dominant process concept; Machine learning; Model structure uncertainty; Parsimonious inductive model

Indexed keywords

DATA MINING; GENETIC ALGORITHMS; HYDROLOGY; LEARNING SYSTEMS; LYSIMETERS; MODEL STRUCTURES; SOIL SURVEYS; SOLVENTS; STATISTICAL TESTS; UNCERTAINTY ANALYSIS;

DATA-BASED MODELLING; DIAGNOSTIC MODEL; DOMINANT PROCESS; INDUCTIVE MODELING; MODEL STRUCTURE UNCERTAINTIES;

GENETIC PROGRAMMING;

DATA MINING; DATA SET; EXPERIMENTAL STUDY; HYDROLOGICAL MODELING; HYPOTHESIS TESTING; IRRIGATION SYSTEM; LYSIMETER; MODEL TEST; PARSIMONY ANALYSIS; PASTURE; PERCOLATION; SOIL TYPE; WATER TABLE;

AUSTRALIA;

EID: 78650708318     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2010.11.009     Document Type: Article

References (23)

1. Babovic V., Keijzer M. Genetic programming as a model induction engine. Journal of Hydroinformatics 2000, 2(1):35-60.
2. Bethune M.G., Selle B., Wang Q.J. Understanding and predicting deep percolation under surface irrigation. Water Resources Research 2008, 44:W12430. 10.1029/2007WR006380.
3. Beven K. How far can we go in distributed hydrological modelling?. Hydrology and Earth System Sciences 2001, 5(1):1-12.
4. Beven K., Freer J. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology 2001, 249(1-4):11-29.
5. Breiman L. Statistical modelling: the two cultures. Statistical Science 2001, 16:199-231.
6. Chatfield C. Model uncertainty, data mining and statistical inference export. Journal of the Royal Statistical Society, Series A (Statistics in Society) 1995, 158(3):419-466.
7. Gaur S., Deo M.C. Real-time wave forecasting using genetic programming. Ocean Engineering 2008, 35(11-12):1166-1172.
8. Holland J.H. Adaptation in Natural and Artificial Systems 1975, University of Michigan Press, Ann Arbor.
9. Khu S.T., Liong S.Y., Babovic V., Madsen H., Muttil N. Genetic programming and its application in real-time runoff forecasting. Journal of American Water Resources Association 2001, 37(2):439-451.
10. Kirchner J.W., Hooper R.P., Kendall C., Neal C., Leavesley G. Testing and validating environmental models. Science of the Total Environment 1996, 183:33-47.
11. Koza J. Genetic Programming: On the Programming of Computers by Natural Selection 1992, MIT Press, Cambridge, MA.
12. Ljung L. System Identication. Theory for the User 1999, Prentice Hall, Upper Saddle River, USA. second ed.
13. Muttil N., Chau K.W. Neural network and genetic programming for modelling coastal algal blooms. International Journal of Environment and Pollution 2006, 28(3-4):223-238.
14. Muttil N., Lee J.H.W. Genetic programming for analysis and real-time prediction of coastal algal blooms. Ecological Modelling 2005, 189(3-4):363-376.
15. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models part I - A discussion of principles. Journal of Hydrology 1970, 10(3):282-290.
16. Parasuraman K., Elshorbagy A., Si B.C. Estimating saturated hydraulic conductivity using genetic programming. Soil Science Society of America Journal 2007, 71(6):1676-1684.
17. Parasuraman K., Elshorbagy A. Toward improving the reliability of hydrologic prediction: model structure uncertainty and its quantification using ensemble-based genetic programming framework. Water Resources Research 2008, 44:W12406. 10.1029/2007WR006451.
18. Silva, S., Costa, E., 2004. Dynamic limits for bloat control. In: Deb, K., et al. (Eds.), Proceedings of GECCO 2004. Springer, Berlin, pp. 666-677.
19. Sivakumar B. Dominant processes concept, model simplification and classification framework in catchment hydrology. Journal Stochastic Environmental Research and Risk Assessment 2008, 22:737-748.
20. Sivapragasam C., Maheswaran R., Venkatesh V. Genetic programming approach for flood routing in natural channels. Hydrological Processes 2008, 22(5):623-628.
21. Venables W.N., Ripley B.D. Modern Applied Statistics with S 2003, Springer, New York.
22. Whigham P.A., Crapper P.F. Modelling rainfall-runoff relationships using genetic programming. Mathematical and Computer Modelling 2001, 33(6-7):707-721.
23. Young P.C. Top-down and data-based mechanistic modelling of rainfall-flow dynamics at the catchment scale. Hydrological Processes 2003, 17:2195-2217.