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Stochastic estimation of states in unconfined aquifers subject to artificial recharge
Author(s) -
Schmidke Klaus D.,
McBean Edward A.,
Sykes Jonathan F.
Publication year - 1982
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/wr018i005p01519
Subject(s) - kalman filter , groundwater recharge , aquifer , ensemble kalman filter , variance (accounting) , extended kalman filter , sensitivity (control systems) , filter (signal processing) , stochastic modelling , mathematics , statistics , computer science , geology , control theory (sociology) , engineering , geotechnical engineering , artificial intelligence , groundwater , accounting , electronic engineering , business , computer vision , control (management)
An extended Kalman filter model for characterizing minimum variance estimates of the piezometric heads and coefficients defining an unconfined aquifer subject to artificial recharge is developed. The system evolution model employs Hantush's (1967) model. Sensitivity analyses are used to test the estimation capability of the technique. The ability of the extended Kalman filter to use all available information from both the system model and measurements of the state to provide approximate minimum variance estimates of the state and confidence limits is shown. The extended Kalman filter version of Hantush's model is applied to a record data length of one year obtained at the study area located in Norwood, Ontario.