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A genetic algorithm is used here to guess-estimate a close-to-true set of trial values as input to a three-staged quasi-linear inverse modeling scheme for the determination of aquifer parameters. To validate the parameter determination, in addition to the conventional measures of misfit root mean squares (rms) and distribution, the aquifer thickness is treated as an unknown parameter and the model...
Use of artificial neural networks (ANNs) is becoming increasingly common in the analysis of groundwater hydrology and water resources problems. In this research, an ANN was developed and used to estimate aquifer parameter values, namely transmissivity and storage coefficient, from pumping test data for a large diameter well. The ANN was trained to map time-drawdown and well diameter data (input vector)...
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