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Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/A3A5EB93-C25A-42AF-8CDE-3AF0DB69B191 | ||
| Year | 2008 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | I. K. Nikolos, M. Stergiadi, M. P. Papadopoulou, G. P. Karatzas, "Artificial Neural Networks an Alternative Approach to Groundwater Numerical Modeling and Environmental Design", Hydrological Processes, Vol. 22, no. 17, pp. 3337-3348, Aug. 2008. DOI: 10.1002/hyp.6916 https://doi.org/10.1002/hyp.6916 | ||
| Appears in Collections |
Classical optimization methodologies based on mathematical theories have been developed for the solution of various constrained environmental design problems. Numerical models have been widely used to represent an environmental system accurately. The use of methodologies such as artificial neural networks (ANNs), which approximate the complicated behaviour and response of physical systems, allows the optimization of a large number of case scenarios with different set of constraints within a short period of time, whereas the corresponding simulation time using a numerical model would be prohibitive. In this paper, a combination of an ANN with a differential evolution algorithm is proposed to replace the classical finite-element numerical model in water resources management problems. The objective of the optimization problem is to determine the optimal operational strategy for the productive pumping wells located in the northern part of Rhodes Island in Greece, to cover the water demand and maintain the water table at certain levels. The conclusions of this study show that the use of ANN as an approximation model could (a) significantly reduce the computational burden associated with the accurate simulation of complex physical systems and (b) provide solutions very close to the optimal ones for various constrained environmental design problems
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