Evaluation of methods for estimating aquifer hydraulic parameters |
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| Affiliation: | 1. Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang, Jiangsu, 212013, China;2. Department of Civil and Environmental Engineering, and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, 96822, USA;3. Department of Civil, Surveying and Environmental Engineering, University of Newcastle, Callaghan, NSW, Australia;4. Department of Civil Engineering, Sharif University of Technology, Tehran, Iran;5. Griffith School of Engineering, Griffith University, Gold Coast Campus, Queensland, QLD 4222, Australia;6. NOAA-Cooperative Remote Sensing Science & Technology Center (NOAA-CREST), City University of New York, NY, USA;1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia;2. Centre for Intelligent Systems Research, Deakin University, Geelong, Victoria, Australia;3. Faculty of Electrical Engineering, Universiti Teknologi MARA, Penang, Malaysia;1. School of Business, Central South University, Changsha 410083, China;2. School of Management, Qingdao Technological University, Qingdao 266520, China;3. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China;1. Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran;2. Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran;3. School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran;1. Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Shahid Fahmideh Street, P.O. Box 65178-38683, Hamedan, Iran;2. Young Researchers and Elite Club, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran;3. Faculty of Electrical Engineering, Babol Nooshirvani University of Technology, Mazandaran, Iran |
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| Abstract: | An accurate estimation of aquifer hydraulic parameters is required for groundwater modeling and proper management of vital groundwater resources. In situ measurements of aquifer hydraulic parameters are expensive and difficult. Traditionally, these parameters have been estimated by graphical methods that are approximate and time-consuming. As a result, nonlinear programming (NLP) techniques have been used extensively to estimate them. Despite the outperformance of NLP approaches over graphical methods, they tend to converge to local minima and typically suffer from a convergence problem. In this study, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) methods are used to identify hydraulic parameters (i.e., storage coefficient, hydraulic conductivity, transmissivity, specific yield, and leakage factor) of three types of aquifers namely, confined, unconfined, and leaky from real time–drawdown pumping test data. The performance of GA and ACO is also compared with that of graphical and NLP techniques. The results show that both GA and ACO are efficient, robust, and reliable for estimating various aquifer hydraulic parameters from the time–drawdown data and perform better than the graphical and NLP techniques. The outcomes also indicate that the accuracy of GA and ACO is comparable. Comparing the running time of various utilized methods illustrates that ACO converges to the optimal solution faster than other techniques, while the graphical method has the highest running time. |
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| Keywords: | Aquifer hydraulic parameters Ant Colony Optimization (ACO) Genetic Algorithm (GA) Nonlinear programming (NLP) Pumping test |
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