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

• Published in: Journal of irrigation and drainage engineering Vol. 129; no. 5; pp. 348 - 359
• Main Authors: Samuel, Manoj P, Jha, Madan K
• Format: Journal Article
• Language: English
• Published: Reston, VA American Society of Civil Engineers 01-10-2003
• Subjects: Agricultural and forest climatology and meteorology. Irrigation. Drainage; Agronomy. Soil science and plant productions; Biological and medical sciences; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Irrigation. Drainage; TECHNICAL PAPERS; Parameter estimation; Hydraulic model; Ground-water management; Parameters; Graphic methods; Aquifers; Graphics; Algorithms; Pumping; Genetic algorithm; Hydraulic models; Water engineering; Pumping tests; Ground water
• ISSN: 0733-9437; 1943-4774
• DOI: 10.1061/(ASCE)0733-9437(2003)129:5(348)

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 leakance (L), 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 under different hydrogeologic conditions by using the GA technique. Applicability, adequacy, and robustness of the developed codes were tested using 12 sets of the published and unpublished aquifer test data. The aquifer parameters were also estimated by the graphical method using AquiferTest software, 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 almost all 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. 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 all three aquifer systems. The GA-based computer programs with interactive windows developed in this study are user-friendly and can serve as a teaching and research tool, which could also be useful for practicing hydrologists and hydrogeologists.