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Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/AACF66A0-457C-4EFC-B33B-290BCFEC4371 | ||
| Year | 2009 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | I.C. Trichakis., I.K. Nikolos, and G.P. Karatzas, "Optimal selection of artificial neural network parameters for the prediction of a karstic aquifer's response," Hydrological Processes, vol. 23, no. 20, pp. 2956–2969, Sept. 2009. doi: 10.1002/hyp.7410 https://doi.org/10.1002/hyp.7410 | ||
| Appears in Collections |
The simulation of karstic aquifers is difficult using traditional groundwater numerical simulators, as the exact knowledge of the hydraulic characteristics of the physical system in small scale is rarely available and the numerical simulators produce results of limited reliability. In the present work, artificial neural networks (ANNs) are utilized to predict the response of a karstic aquifer, using the hydraulic head change per time step rather than the hydraulic head itself as output parameter of the network. As it will be demonstrated, in the first case a better approximation of the physical system's response is achieved as the change of the hydraulic head is more naturally connected to the input parameters of the network, which model the aquatic equilibrium of the system. The correlation of rainfall and hydraulic head change per time step was initially used to determine the time lag of the rainfall input data, which represents the time needed by the rainfall to percolate and reach the water table. In a second step, a differential evolution (DE) algorithm is utilized for the optimal selection of rainfall time lag as well as ANN's architecture and training parameters. Although a time consuming procedure, the improvement obtained suggests that the empirical determination of the ANN parameters and structure is not always sufficient and an optimization procedure, which minimizes the training and evaluation errors of the ANN, may provide substantially better simulation results. The optimized networks were finally used for midterm predictions (30 to 90 days ahead) of the hydraulic head, showing the ability of the ANN with hydraulic head change as output parameter to provide predictions with high accuracy at the end of the considered time period.
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