Abstract
A Differential Evolution (DE) algorithm is combined with an Artificial Neural Network (ANN) to examine different operational strategies for the productive pumping wells located in the Northern part of Rhodes Island in Greece. The objective is to maximize the pumping rate without violating the environmental constraints associated with the water table drawdown at critical locations. The hydraulic head field is simulated using a groundwater flow simulator that solves numerically a system of partial differential equations. Successive calls to the simulator are used to provide the training data to the ANN. Then the ANN is used as an approximation model to the simulator, successively called by the DE algorithm to evaluate candidate solutions. The adopted procedure provides the ability to test different scenarios, concerning the optimization constraints, without retraining of the ANN, which significantly reduces the computational cost of the procedure.
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References
Freeze, R.A., Cherry, J.A.: Groundwater. Prentice-Hall, Inc., New Jersey (1979)
Rogers, L.L., Dowla, F.U.: Optimization of Groundwater Remediation Using Artificial Neural Networks With Parallel Solute Transport Modeling. Water Resources Research 30, 457–481 (1994)
Rogers, L.L., Dowla, F.U., Johnson, V.M.: Optimal Field-Scale Groundwater Remediation Using Neural Networks and the Genetic Algorithm. Environmental Science and Technology 29, 1145–1155 (1995)
Morshed, J., Kaluarachchi, J.J.: Application of Artificial Neural Network and Genetic Algorithm in Flow and Transport Simulations. Advances in Water Resources 22, 145–158 (1998)
Wen, C.-G., Lee, C.-S.: A Neural Network Approach to Multiobjective Optimization for Water Quality Management in a River Basin. Water Resources Research 34, 427–436 (1998)
Aly, A.H., Peralta, R.C.: Comparison of a Genetic Algorithm and Mathematical Programming to the Design of Groundwater Cleanup Systems. Water Resources Research 35, 2415–2425 (1999)
Rao, S.V.N., Thandaveswara, B.S., Murty Bhallamudi, S., Srinivasulu, V.: Optimal Groundwater Management in Deltaic Regions Using Simulated Annealing and Neural Networks. Water Resources Management 17, 409–428 (2003)
Arndt, O., Barth, T., Freisleben, B., Grauer, M.: Approximating a Finite Element Model by Neural Network Prediction for facility Optimization in Groundwater Engineering. Europ. J. of Oper. Research 166, 769–781 (2005)
Yan, S., Minsker, B.: Optimal Groundwater Remediation Design Using an Adaptive Neural Network Genetic Algorithm. Water Resources Research 42 (2006)
Papadopoulou, M.P., Vondikaki, E., Stergiadi, M., Karatzas, G.P.: Optimal Fresh Water Management with Emphasis in Environmental Quality Constraints in the Northern part of Rhodes Island, Greece. In: VIII Protection and Restoration of the Environment Conference, Chania, Greece (2006)
Babu, D.K., Pinder, G.F., Niemi, A., Ahlfeld, D.P., Stothoff, S.A.: Chemical Transport by Three-Dimensional Groundwater Flows, 84-WR-3. Princeton University, USA (1997)
Nikolos, I.K., Stergiadi, M., Papadopoulou, M.P., Karatzas, G.P.: Artificial Neural Networks as an Alternative Approach to Groundwater Numerical Modelling and Environmental Design. Hydrological Processes (2008)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution, a Practical Approach to Global Optimization. Springer, Heidelberg (2005)
Nikolos, I.K.: Inverse Design of Aerodynamic Shapes using Differential Evolution coupled with Artificial Neural Network. In: Proceedings of the ERCOFTAC Conference in Design Optimization: Methods and Applications, Athens (2004)
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Nikolos, I.K., Stergiadi, M., Papadopoulou, M.P., Karatzas, G.P. (2008). Groundwater Numerical Modeling and Environmental Design Using Artificial Neural Networks and Differential Evolution. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_5
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DOI: https://doi.org/10.1007/978-3-540-85565-1_5
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