![]() The results suggest that increasing the model complexity may lead to better prediction accuracy but at an expense of increasing uncertainty. While the first set uses just the additive operators, the second set uses both the additive and the multiplicative operators to define the predictand-predictor relationship. 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 predictand-predictor relationship. Eddy-covariance-measured actual evapotranspiration is modeled as a function of net radiation, air temperature, ground temperature, relative humidity, and wind speed. The performance of the proposed modeling framework is analyzed with regards to its ability in characterizing the evapotranspiration process at the Southwest Sand Storage facility, located near Ft. The resulting ensemble of models is then used to quantify the uncertainty associated with the model structure. The modeling framework is based on initially generating different realizations of the original data set using a non-parametric bootstrap method, and then exploiting the ability of the self-organizing algorithms, namely genetic programming, to evolve their own model structure for each of the resampled data sets. In this paper, a modeling framework that can explicitly account for the effect of model structure uncertainty has been proposed. 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. Uncertainty analysis is starting to be widely acknowledged as an integral part of hydrological modeling.
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