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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/2C3DAD16-FF4F-458E-A81C-5C18B451D4B5 | - |
| Identifier | http://www.researchgate.net/publication/236962287_Simulation_of_a_Karstic_Groundwater_System_using_Artificial_Neural_Networks_(ANNs)._The_example_of_Edwards_aquifer | - |
| Language | en | - |
| Title | Simulation of a karstic groundwater system using artificial neural networks (ANNs). The example of Edwards aquifer | en |
| Creator | Nikolos Ioannis | en |
| Creator | Νικολος Ιωαννης | el |
| Creator | George P Karatzas | en |
| Creator | Ioannis Trichakis | en |
| Publisher | European Water Resources Association (EWRA) | en |
| Content Summary | In the recent past, Artificial Neural Networks (ANNs) have found application in many hydrological problems. In groundwater management ANNs are usually used to predict the hydraulic head at a well location. The reason behind using ANNs is that numerical groundwater modeling may be very difficult to implement in karstic regions as the exact geometry of the flow conduits is rarely available and even when it is available in detail the aquifer is still very difficult to be simulated. A black box approach is used by ANNs to simulate the hydraulic head, taking as input hydrological parameters such as rainfall and temperature, as well as hydrogeological parameters such as pumping rates from nearby wells. Measured data from Edward’s aquifer in Texas, USA are used in this work to train and evaluate an ANN. The Edwards Aquifer is a unique groundwater system and one of the most prolific artesian aquifers in the world. The present work focuses on simulation of hydraulic head change at an observation well in the area. The adopted ANN is a classic fully connected multilayer perceptron, with two hidden layers. All input parameters are directly or indirectly connected to the aquatic equilibrium and the ANN is treated as a sophisticated analogue to empirical models of the past. A correlation analysis of the measured data is used to determine the time lag between the current day and the day used for input of the measured rainfall levels. After the calibration process the testing data were used in order to check the ability of the ANN to interpolate or extrapolate in other regions, not used in the training procedure. The results show that there is a need for exact knowledge of pumping from each well in karstic aquifers as it is difficult to simulate the sudden drops and rises, which in this case can be more than 6 ft (approx. 2m). That aside, the ANN is still a useful tool to simulate karstic aquifers that are difficult to be simulated by numerical groundwater models. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-11-04 | - |
| Date of Publication | 2009 | - |
| Bibliographic Citation | I.C. Trichakis, I.K. Nikolos, G.P. Karatzas. (2009, June). Simulation of a Karstic Groundwater System using Artificial Neural Networks (ANNs). The example of Edwards aquifer. Presenetd at 7th International Conference: Water Resources Conservation and Risk Reduction Under Climatic Instability. [Online]. Available: http://www.researchgate.net/publication/236962287_Simulation_of_a_Karstic_Groundwater_System_using_Artificial_Neural_Networks_(ANNs)._The_example_of_Edwards_aquifer | en |
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