Estimation of Partially Penetrated Aquifer Parameters from Pumping Test Data by Genetic Algorithm Optimization Technique

Adequate and reliable estimate of aquifer parameters are of utmost importance for proper management of groundwater resources. The pumping test is the standard technique for estimating various hydraulic properties of aquifer systems, viz., transmissivity, hydraulic conductivity, storage coefficient, and thickness of the aquifer, for which the graphical method is widely used. In the present study, the efficacy of the genetic algorithm (GA) optimization technique is assessed in estimating aquifer parameters from the time-drawdown pumping test data. Computer codes were developed to optimize aquifer parameters of a partially penetrated confined aquifer by using the GA technique. Applicability, adequacy, and robustness of the developed codes were tested using two sets of field data. The aquifer parameters were also estimated by the graphical method, and compared with those obtained by the GA technique. The GA technique yielded significantly low values of the sum of square errors (SSE) for both the datasets. The results revealed that the GA technique is an efficient and reliable method for estimating aquifer parameters, for when the graphical matching is poor. Also, it was found that because of its inherent characteristics, GA avoids the subjectivity, long computation time and ill-posedness often associated with conventional optimization techniques. Furthermore, the performance evaluation of the developed GA-based computer codes showed that the fitness value (SSE) of the best point in a population reduces with increasing generation number and population size. The analysis of the sensitivity of the parameters during the performance of GA indicated that a unique set of aquifer parameters was obtained for both the datasets.