Testing the structure of a hydrological model using Genetic Programming

Selle, Benny and Muttil, Nitin (2011) Testing the structure of a hydrological model using Genetic Programming. Journal of Hydrology, 397 (1-2). pp. 1-9. ISSN 0022-1694

Abstract

Genetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that Genetic Programming can be used to test the structure of hydrological models and to identify dominant processes in hydrological systems. To test this, Genetic Programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, watertable depths and water ponding times during surface irrigation. Using Genetic Programming, a simple model of deep percolation was recurrently evolved in multiple Genetic Programming runs. This simple and interpretable model supported the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that Genetic Programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/7620
DOI https://doi.org/10.1016/j.jhydrol.2010.11.009
Official URL http://www.sciencedirect.com/science/article/pii/S...
Subjects Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Historical > FOR Classification > 0905 Civil Engineering
Historical > SEO Classification > 9609 Land and Water Management
Keywords ResPubID22835, data mining, machine learning, diagnostic model evaluation, model structure uncertainty, parsimonious inductive model, data-based modelling, dominant process concept
Citations in Scopus 21 - View on Scopus
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