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Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response

Trichakis Ioannis, Nikolos Ioannis, Karatzas Giorgos

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/0C32C1C4-3C4F-4B80-8BEE-E7FEF0FA128D
Έτος 2011
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά I.C. Trichakis, I.K. Nikolos, and G.P. Karatzas,"Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response," Hydrological Processes, vol. 25, no. 18, pp. 2827–2836, Aug. 2011. doi: 10.1002/hyp.8044 https://doi.org/10.1002/hyp.8044
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Περίληψη

Following many applications artificial neural networks (ANNs) have found in hydrology, a question has been rising for quantification of the output uncertainty. A pre-optimized ANN simulated the hydraulic head change at two observation wells, having as input hydrological and meteorological parameters. In order to calculate confidence intervals (CI) for the ANN output two bootstrap methods were examined namely bootstrap percentile and BCa (Bias-Corrected and accelerated). The actual coverage of the CI was compared to the theoretical coverage for different certainty levels as a means of examining the method's reliability. The results of this work support the idea that the bootstrap methods provide a simple tool for confidence interval computation of ANNs. Comparing the two methods, the percentile requires fewer calculations and yields narrower intervals with similar actual coverage to that of BCa. Overall, the actual coverage was proved lower than desired when not modeled points were present in the data subset.

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