Using a hybrid approach to optimize experimental network design for aquifer parameter identification.
This research develops an optimum design model of groundwater network using genetic algorithm (GA) and modified Newton approach, based on the experimental design conception. The goal of experiment design is to minimize parameter uncertainty, represented by the covariance matrix determinant of estimated parameters. The design problem is constrained by a specified cost and solved by GA and a parameter identification model. The latter estimates optimum parameter value and its associated sensitivity matrices. The general problem is simplified into two classes of network design problems: an observation network design problem and a pumping network design problem. Results explore the relationship between the experimental design and the physical processes. The proposed model provides an alternative to solve optimization problems for groundwater experimental design.
Chang
Liang-Cheng
LC
Department of Civil Engineering, National Chiao Tung University, Hsinchu, Taiwan, Republic of China. lcchang31938@gmail.com
Chu
Hone-Jay
HJ
Lin
Yu-Pin
YP
Chen
Yu-Wen
YW
D000465:Algorithms; D004784:Environmental Monitoring / Q000379:methods*; D005618:Fresh Water / Q000737:chemistry*; D008962:Models, Theoretical; D014872:Water Movements