Toward improving the reliability of hydrologic prediction: Model structure uncertainty and its quantification using ensemble-based genetic programming framework,

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@Article{Parasuraman:2008:WRR,

• author = "Kamban Parasuraman and Amin Elshorbagy",
• title = "Toward improving the reliability of hydrologic prediction: Model structure uncertainty and its quantification using ensemble-based genetic programming framework,",
• journal = "Water Resources Research",
• year = "2008",
• volume = "44",
• pages = "W12406",
• month = "5 " # dec,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.agu.org/pubs/crossref/2008/2007WR006451.shtml",
• DOI = "doi:10.1029/2007WR006451",
• abstract = "Uncertainty analysis is starting to be widely acknowledged as an integral part of hydrological modelling. The conventional treatment of uncertainty analysis in hydrologic modeling is to assume a deterministic model structure, and treat its associated parameters as imperfectly known, thereby neglecting the uncertainty associated with the model structure. In this paper, a modelling framework that can explicitly account for the effect of model structure uncertainty has been proposed. The modelling framework is based on initially generating different realisations of the original data set using a non-parametric bootstrap method, and then exploiting the ability of the self-organising algorithms, namely genetic programming, to evolve their own model structure for each of the resampled data sets. The resulting ensemble of models is then used to quantify the uncertainty associated with the model structure. The performance of the proposed modelling framework is analysed with regards to its ability in characterising the evapotranspiration process at the Southwest Sand Storage facility, located near Ft. McMurray, Alberta. Eddy-covariance-measured actual evapotranspiration is modelled as a function of net radiation, air temperature, ground temperature, relative humidity, and wind speed. Investigating the relation between model complexity, prediction accuracy, and uncertainty, two sets of experiments were carried out by varying the level of mathematical operators that can be used to define the predict and-predictor relationship. While the first set uses just the additive operators, the second set uses both the additive and the multiplicative operators to define the predict-and-predictor relationship. The results suggest that increasing the model complexity may lead to better prediction accuracy but at an expense of increasing uncertainty. Compared to the model parameter uncertainty, the relative contribution of model structure uncertainty to the predictive uncertainty of a model is shown to be more important. Furthermore, the study advocates that the search to find the optimal model could be replaced by the quest to unearth possible models for characterising hydrological processes.",

}

Genetic Programming entries for Kamban Parasuraman Amin Elshorbagy

Citations