Wu, Yu ZhangYu ZhangWuHWA-LUNG YUCheung, Shao YongShao YongCheung2023-06-082023-06-082016-12-0102575744https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057146847&partnerID=40&md5=b174734ba2c2b6cbd474bc57cb245c71https://scholars.lib.ntu.edu.tw/handle/123456789/631985Owing to the limitation of hydrogeological observations as well as high uncertainty, groundwater model parameter estimation has been an important issue. Among all parameter assessment models, Kalman filter could provide a real-time geological parameter estimation method, such as Extended Kalman Filer and Ensemble Kalman Filter, which are widely applied into the research of groundwater parameter estimation �recently. In this paper, the numerical experiments are conducted in a synthetic two-dimensional confined aquifer. Through three different cases to test the performances of Extended Kalman Filter integrating information from different sources. �In case one, only the data of observation well is adopted. As for case two, we add the data of the boundary condition of water head. In case three, hydraulic conductivity data is integrated into the experiment. These three cases used all water head data as well as Mean Square Errors(MSE) of hydraulic conductivity in the grids of the water field, which �the MSE is used as an index to evaluate the test errors. The result showed that (1) In the estimation of water head and hydraulic conductivity, case three has the most source of data and also the least MSE, on the contrary, case one has the least source of data and themost MSE. Therefore, using the Extended Kalman Filer could integrate data from different sources and also improve the accuracy of estimating water head and geological parameter. (2) In case one and two, the MSE of hydraulic conductivity in the same month of each year is declining, which means that the Extended Kalman Filer could use variable water head information to estimate hydraulic conductivity accurately.Calibrations of parameters; Extended Kalman Filer; Groundwater modeljournal article2-s2.0-85057146847https://api.elsevier.com/content/abstract/scopus_id/85057146847