Spatial data analysis methods have many applications in various fields‎, ‎such as agriculture‎, ‎mining engineering‎, ‎and meteorology‎. ‎In this study‎, ‎ordinary Kriging and indicator Kriging are considered to predict alumina grade in the Jajarm mine in Iran‎, ‎and the precision of the methods is computed‎. ‎A conditional simulation is carried out based on the data set for a more general comparison of ordinary and indicator Kriging to interpolate Alumina grade in the mine‎. ‎In the case of monitoring possible variation related to sample size and type of variogram model‎, ‎simulations are performed with various sample sizes and different types of variogram models‎. ‎Then ordinary and indicator Kriging methods are applied for every set of simulated data (concerning different sample sizes and types of variogram models)‎, ‎and root of standardized mean square error prediction is considered as a cross-validation criterion to compare the Kriging methods‎. ‎The simulation results show that under the assumptions‎, ‎ordinary Kriging has better performance than the indicator Kriging method.

In this research, a hybrid wavelet-artificial neural network (WANN) and a geostatistical method were proposed for spatiotemporal prediction of the groundwater level (GWL) for one month ahead. For this purpose, monthly observed time series of GWL were collected from September 2005 to April 2014 in 10 piezometers around Mashhad City in the Northeast of Iran. In temporal forecasting, an artificial neural network (ANN) and a WANN were trained for each piezometer. Kriging was used in spatial estimations. The comparison of the prediction accuracy of these two models illustrated that the WANN was more efficacious in prediction of GWL for one month ahead. Thereafter, in order to predict GWL in desired points in the study area, the Kriging method was used and a Gaussian model was selected as the best variogram model. Ultimately, the WANN with coefficient of determination and root mean square error and mean absolute error, 0.836 and 0.335 and 0.273 respectively, in temporal forecasting and Gaussian model with root mean square, 0.253 as the best fitted model on Kriging method for spatial estimating were suitable choices for spatiotemporal GWL forecasting. The obtained map of groundwater level showed that the groundwater level was higher in the areas of plain located in mountainside areas. This fact can show that outcomes are respectively correct.