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

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dc.contributor.author Alemayehu, S. A.
dc.contributor.author Prasad, K. S. Hari
dc.contributor.author Ojha, C. S. P.
dc.date.accessioned 2020-08-19T10:31:35Z
dc.date.available 2020-08-19T10:31:35Z
dc.date.issued 2009
dc.identifier.uri http://117.252.14.250:8080/jspui/handle/123456789/4676
dc.description.abstract Adequate and reliable estimates of aquifer parameters are of utmost importance for proper management of vital groundwater resources. The pumping (aquifer) test is the standard technique for estimating various hydraulic properties of aquifer systems, viz., transmissivity (T), hydraulic conductivity (K), storage coefficient (S), and thickness of the aquifer (H), 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 various aquifer parameters of partially penetrated confined aquifer by using the GA technique. Applicability, adequacy, and robustness of the developed codes were tested using two sets field test data. The aquifer parameters were also estimated by the graphical method, and were 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 under study. The results revealed that the GA technique is an efficient and reliable method for estimating various aquifer parameters, especially in the situation 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. en_US
dc.language.iso en en_US
dc.publisher Allied Publishers Pvt. Ltd., New Delhi en_US
dc.subject Ground Water en_US
dc.subject Aquifer Parameters en_US
dc.subject Pumping Test Data en_US
dc.subject Genetic Algorithm Optimization Technique en_US
dc.title 52-Estimation of Partially Penetrated Aquifer Parameters from Pumping Test Data by Genetic Algorithm Optimization Technique . en_US
dc.type Other en_US


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