Evaluation of Traditional and Nontraditional Optimization Techniques for Determining Well Parameters from Step-Drawdown Test Data

Adequate knowledge of the hydraulic characteristics of production wells is indispensable for the proper development and management of wells and for the selection of suitable pumps. In this study, the characteristic hydraulic parameters of production wells were determined by the widely used graphical analysis of step-drawdown pumping test data as well as the two traditional gradient-based nonlinear optimization techniques (viz., Levenberg–Marquardt and Gauss–Newton) and the nontraditional optimization technique, genetic algorithm. Three stand-alone and interactive computer programs were developed to optimize the hydraulic parameters of production wells by these numerical techniques. The efficacy and robustness of the developed computer codes were examined using eleven sets of step-drawdown data from diverse hydrogeologic conditions. The results of this study revealed that all the three numerical techniques yielded superior well parameters with lower values of root-mean-square errors (RMSE) for all the elev...

[1]  S. Ranjithan,et al.  Using genetic algorithms to solve a multiple objective groundwater pollution containment problem , 1994 .

[2]  Wolfgang Kinzelbach,et al.  Groundwater Modelling: An Introduction With Sample Programs in Basic , 1986 .

[3]  R. Wardlaw,et al.  EVALUATION OF GENETIC ALGORITHMS FOR OPTIMAL RESERVOIR SYSTEM OPERATION , 1999 .

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[6]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[7]  S. Mohan,et al.  Parameter Estimation of Nonlinear Muskingum Models Using Genetic Algorithm , 1997 .

[8]  A. Cheng,et al.  Pumping optimization in saltwater‐intruded coastal aquifers , 2000 .

[9]  Vedat Batu Aquifer Hydraulics: A Comprehensive Guide to Hydrogeologic Data Analysis , 1998 .

[10]  J. Eheart,et al.  Using Genetic Algorithms to Solve a Multiobjective Groundwater Monitoring Problem , 1995 .

[11]  W. Yeh Review of Parameter Identification Procedures in Groundwater Hydrology: The Inverse Problem , 1986 .

[12]  K. Abbaspour,et al.  A sequential uncertainty domain inverse procedure for estimating subsurface flow and transport parameters , 1997 .

[13]  B. Minsker,et al.  Cost‐effective long‐term groundwater monitoring design using a genetic algorithm and global mass interpolation , 2000 .

[14]  Madan K. Jha,et al.  Estimation of Aquifer Parameters from Pumping Test Data by Genetic Algorithm Optimization Technique , 2003 .

[15]  Jin-Ping Gwo,et al.  In search of preferential flow paths in structured porous media using a simple genetic algorithm , 2001 .

[16]  David E. Goldberg,et al.  Designing a competent simple genetic algorithm for search and optimization , 2000 .

[17]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[18]  Madan K. Jha,et al.  Field Investigations for Sustainable Groundwater Utilization in the Konan Basin , 1999 .

[19]  K. Lakshmi Prasad,et al.  Estimating net aquifer recharge and zonal hydraulic conductivity values for Mahi Right Bank Canal project area, India by genetic algorithm , 2001 .

[20]  A. Mayer,et al.  Pump‐and‐treat optimization using well locations and pumping rates as decision variables , 1997 .

[21]  Cass T. Miller,et al.  Rapid Solution of the Nonlinear Step-Drawdown Equation , 1983 .

[22]  Q. J. Wang The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[23]  David E. Goldberg,et al.  Risk‐based in situ bioremediation design using a noisy genetic algorithm , 2000 .

[24]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[25]  S. Sherif,et al.  Groundwater Hydraulics and Pollutant Transport , 1999 .

[26]  Weiqi Wang,et al.  Optimal groundwater remediation with well location as a decision variable: Model development , 1994 .

[27]  Li Chen,et al.  Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management , 1998 .

[28]  Dragan Savic,et al.  WATER NETWORK REHABILITATION WITH STRUCTURED MESSY GENETIC ALGORITHM , 1997 .

[29]  David P. Ahlfeld,et al.  Well Location in Capture Zone Design Using Simulation and Optimization Techniques , 1990 .

[30]  Jiaping Yang,et al.  Structural Optimization by Genetic Algorithms with Tournament Selection , 1997 .

[31]  T. Culver,et al.  Constraint Handling for Genetic Algorithms in Optimal Remediation Design , 2000 .

[32]  Miguel A. Mariño,et al.  Stochastic Solution to Inverse Problem in Ground Water , 1997 .

[33]  Miguel A. Mariño,et al.  Estimation of Spatially Variable Aquifer Hydraulic Properties Using Kalman Filtering , 1997 .

[34]  W. Yeh,et al.  Identification of Parameter Structure in Groundwater Inverse Problem , 1985 .

[35]  Otto J. Helweg,et al.  Water Resources Planning and Management , 1985 .

[36]  Govinda C. Mishra,et al.  Estimation of Hydraulic Diffusivity in Stream-Aquifer System , 1999 .

[37]  Godfrey A. Walters,et al.  Groundwater optimization and parameter estimation by genetic algorithm and dual reciprocity boundary element method , 1996 .

[38]  M. I. Rorabaugh Graphical and Theoretical Analysis of Step-Drawdown Test of Artesian Well , 1953 .

[39]  C. E. Jacob Drawdown Test to Determine Effective Radius of Artesian Well , 1947 .

[40]  Subhash Chander,et al.  Analysis of Pumping Test Data Using Marquardt Algorithm , 1981 .

[41]  Otto J. Helweg,et al.  Step‐Drawdown Test Analysis by Computer , 1975 .

[42]  D. McKinney,et al.  Genetic algorithm solution of groundwater management models , 1994 .

[43]  Jagath J. Kaluarachchi,et al.  Enhancements to Genetic Algorithm for Optimal Ground-Water Management , 2000 .