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Least cost design of groundwater quality monitoring networks
Author(s) -
Zhang Yingqi,
Pinder George F.,
Herrera Graciela S.
Publication year - 2005
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2005wr003936
Subject(s) - latin hypercube sampling , kalman filter , groundwater , sampling (signal processing) , field (mathematics) , groundwater flow , computer science , covariance , mathematical optimization , environmental science , filter (signal processing) , statistics , monte carlo method , mathematics , aquifer , engineering , geotechnical engineering , pure mathematics , computer vision
A genetic algorithm combined with a static Kalman filter and a stochastic groundwater flow and contaminant transport model can be used to determine when and where to take samples to reduce the uncertainty associated with the statistical distribution describing a groundwater contamination concentration field at least cost. The use of a Kalman filter requires an initial estimate of the concentration field as well as its error covariance matrix, which are generated as the output of the simulation model. The random field input to the simulator, namely, realizations of the hydraulic conductivity field, can be generated using a Latin hypercube sampling technique. Employing the proposed optimal design strategy, a cost‐effective groundwater sampling network design is realized. The proposed methodology is applied to a field problem.