Application of Bayesian Maximum Entropy Filter in parameter calibration of groundwater flow model in Choshui River Alluvial Fan

Due to the limited hydrogeological observation and its high levels of uncertainty, therefore use of groundwater model to estimate hydrogeological study has been an important issue. There are many methods of parameter estimation, Kalman filter provides a real-time calibration of parameters through measurement of groundwater, related methods such as Extended Kalman Filter and Ensemble Kalman Filter are widely applying in groundwater research. However, due to the properties of the high uncertainty of hydrogeological data, Kalman Filter method does not consider the uncertainty of data. Bayesian Maximum Entropy Filtering provides a method can consider the uncertainty of data to parameter estimation. With this two methods, we can estimate parameter by given hard data and soft data in the same time steps. In this study, we use Python and QGIS in groundwater model (MODFLOW) and development of Extended Kalman Filter and Bayesian Maximum Entropy Filtering in Python in parameter estimation. This method may provide a conventional filtering method and also consider the uncertainty of data. This study was conducted through numerical model experiment to explore, combine Bayesian maximum entropy filter and a hypothesis for the architecture of MODFLOW groundwater model numerical estimation. Through the virtual observation wells to simulate and observe the groundwater model periodically. The result showed that considering the uncertainty of data, the Bayesian maximum entropy filter will provide an ideal result of real-time parameters estimation. There are total three cases in our research, which is the virtual case I, virtual case 2 and a real case. There are six cases in virtual case I. In case 1, input data are all hard data which is hydraulic water level, hydraulic conductivity, and specific storage; in case2 to case 4, we add soft data of boundary water level , hydraulic conductivity, and specific storage succesively. In case 5and 6, we remove the soft data of boundary water level; but in case 6, we assume that model boundary water level does not change over time. There is three cases in virtual case II, In this case, we only consider minimum data. In case 7,we only input hard data of hydraulic water level ; in case 8 and 9,we inout soft data of boundary water level and hydraulic conductivity. In real case, we use the data of obersvation data of water level from year 1992 to 2011; hard data of hydraulic conductivity and specific storage from Central Geological Servey.With the use of above data in modeling, create groundwater model with MODFLOW and integrate with Extended Kalman Filter and Bayesian Maximum Entropy Filtering in parameter estimation. Results show that in virtual case I and II, with the use of soft data in parameter estimation, will increase the precision of parameter estimation. But in reality, we do not have so much information. Therefore, in case 7,we only use the hard data of observation data of water level in parameter estimation. Eventhough, the effect of paramter estimation during prophase is not good, but with the time over in updating, the effect of parameter estimation has well performance. Therefore, in the case of lack of information, this method also can use is parameter estimation.