Prediction the Groundwater Depth using Kriging Method and Bayesian Kalman Filter Approach in Erbil Governorate

The aim of this research is using the kriging method as one of geostatistics interpolation methods on the measured value of the specific part and Bayesian Kalman filter to identifying the depth of Groundwater in Erbil. Geostatistics is a tool which is developed for statistical analysis of any continuous data that can be measured at any location in the space. The Kalman filter is the Bayesian optimum solution to the problem of estimating the unknown state of a dynamic system from noisy data and is more efficient than computing the estimate directly from the entire past observed data. The main goal of this work is to predict anew value at the unmeasured location by kriging method and Bayesian Kalman filter and compare these two methods. The dataset is the observed values of the (295) wells that had been taken from a known specific place which called Shaqlawa – in Erbil Governorate. The comparison was done by calculating mean absolute error (MAE) and root mean square error (RMSE) for the value of the depth of groundwater in the eara of the study. The values of (MAE and RMSE) of each models are compared and the smaller values of them are the better interpolation as it shown in analyzing to evaluate the precision of the prediction.