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- W45311683 abstract "In the first phase of the research, we have developed a complementary modeling framework to handle systematic error in physically-based groundwater flow model applications that uses data-driven models of the errors during the calibration phase. The effectiveness of four error-correcting data-driven models, namely, artificial neural networks (ANN), support vector machines (SVM), decision trees (DT) and instance based weighting (IBW) is examined for forecasting MODFLOW head prediction errors and subsequently updating the head predictions at existing and proposed observation wells. In the second phase of the research, the uncertainty of the complementary modeling framework is assessed using three alternative methods. The first method estimates the combined MODFLOW-ANN model confidence and prediction intervals using first-order least-squares regression approximation theory. The second method uses Monte Carlo and bootstrap techniques for MODFLOW and ANN, respectively, to construct the combined model confidence and prediction intervals. The third method relies on a Bayesian approach that uses analytical or Monte Carlo methods to derive the intervals. Further, the performance of these approaches is compared with Generalized Likelihood Uncertainty Estimation (GLUE) and Maximum Likelihood Bayesian Model Averaging (MLBMA) intervals of the MODFLOW predictions alone. Finally, the applicability of the methodologies and the validity of the complementary modeling framework are tested using both hypothetical and real-world groundwater flow problems of varying complexity. The results indicate that the complementary modeling framework presents a promising and viable alternative to improve groundwater flow predictions, especially, those related to long-term temporal predictions at observation wells and spatial predictions at arbitrary locations. For the real-world groundwater flow problem, the complementary modeling framework reduced MODFLOW's root-mean-square errors (RMSE) for temporal and spatial head predictions by about 78% and 67%, respectively. The uncertainty analysis techniques also significantly improve the estimated 95% confidence and predictions intervals. The percentage of data coverage by the intervals is improved by as much as 88%, while the width of the intervals is diminished." @default.
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- W45311683 date "2008-01-01" @default.
- W45311683 modified "2023-09-27" @default.
- W45311683 title "Data-driven models to enhance physically-based groundwater model predictions" @default.
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