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Comparison of a black-box model to a traditional numerical model for hydraulic head prediction


Two different methodologies for hydraulic head simulation were compared in this study. The first methodology is a classic numerical groundwater flow simulation model, Princeton Transport Code (PTC), while the second one is a black-box approach that uses Artificial Neural Networks (ANNs). Both methodologies were implemented in the Bavaria region in Germany at thirty observation wells. When using PTC, meteorological and geological data are used in order to compute the simulated hydraulic head following the calibration of the appropriate model parameters. The ANNs use meteorological and hydrological data as input parameters. Different input parameters and ANN architectures were tested and the ANN with the best performance was compared with the PTC model simulation results. One ANN was trained for every observation well and the hydraulic head change was simulated on a daily time step. The performance of the two models was then compared based on the real field data from the study area. The cases in which one model outperforms the other were summarized, while the use of one instead of the other depends on the application and further use of the model.

Journal Article Type Article
Publication Date 2016
Journal Global Nest Journal
Print ISSN 1790-7632
Peer Reviewed Peer Reviewed
Volume 18
Issue 4
Pages 761-770
APA6 Citation Tapoglou, E., Chatzakis, A., & Karatzas, G. (2016). Comparison of a black-box model to a traditional numerical model for hydraulic head prediction. Global NEST journal, 18(4), 761-770.
Keywords Artificial neural network; Groundwater modeling; Hydraulic head change simulation; Princeton transport code
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Copyright Statement
Copyright© 2016 Global NEST

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