For spatial data that are correlated in terms of their locations in the underlined space the moving block bootstrap METHOD is usually used to estimate the precision measures of the estimators. But in this METHOD, the boundary observations have less chance of presence in blocks resampling than the other observations. In this paper, an algorithm is given for separate block bootstrap to estimate the precision measures of KRIGING spatial predictor. Then, it is shown that the bias estimation of KRIGING with separate block bootstrap METHOD is unbiased and the KRIGING variance estimator is consistent. Finally, in a simulation study, the efficiencies of the separate and moving block bootstrap METHODs are compared for measures of estimation precision.

In this paper, KRIGING has been chosen as the METHOD for surrogate construction. The basic idea behind KRIGING is to use a weighted linear combination of known function values to predict a function value at a place where it is not known. KRIGING attempts to determine the best combination of weights in order to minimize the error in the estimated function value. Because the actual function value is not known, the error is modeled using probability theory and then minimized. The result is a linear system of equations that can be solved to find a unique combination of weights for a given point at which interpolation is to be performed.

The water resources are limited and the groundwater levels decrease due to water abuse. This causes the lack of water problem in some regions of Iran. Therefore the hydrological and statistical METHODs needed for prediction of the water levels at a given location, region or times. The groundwater levels are variables changing by space and time, and their data can be considered as a spatial-temporal data set. Modeling of the correlation structures of such data is a major tool, for the prediction of unknown water level at some specified locations and times. This correlation structure is specified by fitting suitable variogram or covariogrammodels to the data. In this article some nonseparable covariance models were briefly reviewed. Then the spatial-temporal variogram of underground water levels were estimated as product and sum-product models. Finally the underground water levels of Birjand region was predicted using the universal KRIGING and give the contour map at the selected time. Moreover the prediction precision of different models were compared numerically.

Background and Objectives: Groundwater as one of the valuable water resources that has always been of interest to researchers. Preparing an interpolated water level zoning map is one of fields that can be acquired via best interpolation METHODs among different available METHODs. The KRIGING METHOD, based on semi variogram analysis, is one of traditional geostatistical METHODs. Interpolation precision is depended on the suitable selection of variogram. Empirical Bayesian KRIGING (EBK) METHOD is developed to estimated semi variogram parameters during simulation process. The objective of this study is investigation of EBK to increase the precision of groundwater level interpolation zoning. For this purpose, the results of this study were compared with the results of other other deterministic interpolation METHOD including inverse distance weighted, radial basis function and local and global polynomial functions. Materials and METHODs: This study was carried out based on the annual mean groundwater level of 57 deep well in Ghareh-Sou aquifer located in Golestan province during 2005-2016 period. In order to groundwater level zoning, different deterministic and geostatistical interpolation METHODs were evaluated using cross validation technique. The best semi variogram selected for KRIGING and EBK METHODs and finally the model with minimum error was determined and mapped. Results: The results of cross validation in study area showed that among deterministic METHOD, degree two local polynomial METHOD has higher accuracy and even less error in comparison with KRIGING METHOD. The EBK METHODs, with simulation of fitness of suitable variogram on groundwater level data, led to decrease in KRIGING error (23 to 16 meter) and had close precision to local polynomial METHOD. Conclusion: Although the error of the maps prepared based on EBK and local polynomial METHODs have no significant differences, there are considerable discrepancies between these maps. The EBK basis map shows smoother spectrum of groundwater level changes and the drawn pattern with this METHOD is also proportional to general slope of study area.

This study uses multiple indicator KRIGING estimator based on assayed data acquired from 48 exploration boreholes in Tabas coal mine in order to predict gas content of coal seams. Results of the estimated block models showed that approximately 12% of the total area has the gas content of less than 5 (m3/ton) (Low risk), 11% has 5-10 (m3/ton) (Medium Risk) and 15% has 10-15 (m3/ton) (High Risk), while about 62% of total area has the gas content of more than 15(m3/ton) which is of a high risk. Therefore, according to the experiences from mined panels, in the zones with lower to median gasification risk, it is possible to keep the mining operation running with management and optimization of the ventilation system. However, in the zones with higher gasification rate, demethanization process is mandatory.

Groundwater level variations can essentially affect the execution of many engineering projects. Accordingly, due to the projects underway in Tabriz district and especially Tabriz Underground Project (METRO), spatiotemporal prediction of the groundwater level is crucial. Due to the aquifer complexity in the Tabriz area, there are problems in using classical mathematical models. In this research a combination of the artificial neural networks and Geostatistic models were applied as a new METHOD for spatiotemporal prediction of groundwater levels using selected piezometers. For this purpose, the different neural networks were examined for groundwater level forecasting in central piezometer and an optimal ANN architecture was identified. This ANN structure was then used for modeling the selected piezometers. The results of these models were used as the inputs of the geostatistics model for forecasting spatial groundwater level in the study area. Two year monthly groundwater level prediction data in selected piezometers resulted by ANN modeling were among these input data. In order to obtain a high efficiency model, different METHODs of the geostatistic model were used. Finally the obtained model was tested by water level data in piezometers other than those used for model calibration. The results of this hybrid model were acceptable.

Regional analysis is the stability METHOD to improve estimates of flood frequency, which has become one of the dynamic sectors in hydrology and the new theories are testing, constantly. Application of geostatistical METHOD is an innovation in this field for regional flood analysis. This technique is based on the interpolation of hydrological variables in the physiographical space instead of using common geographical space. However, the hydrological, climatological and physiographical information of 38 river basin information of watersheds inside of Mazandaran province were obtained and Canonical KRIGING METHOD was used for regional flood analysis whit return periods of 10, 20, 50 and 100 years. The Canonical Correlation Analysis was used to design physiographic space by the geomorphoclimatical variables affecting the flood. The Gaussian model had the best fit to semi-variogram model in all return periods and the geostatistical METHOD of Ordinary KRIGING was used for regionalization. Results evaluated using Jack Knife mutual procedure and the five statistical indexes. The performance of NASH was obtained over than 0.9 in all of return periods, which implies the accurate and acceptable predictions of flood in ungauged basins. The other indicators led to satisfying results, too. According to the results of relative statistical indicators accuracy regional estimates improves as long as return periods increase. These results showed that application of Canonical KRIGING METHOD is effective and practical approach for regional flood analysis.

Due to the importance of groundwater resources in arid and semi arid regions, the qualitative assessment of these resources is so important. Pressurized irrigation as a suitable METHOD for increasing irrigation efficiency and reducing water consumption, requires water quality analysis. Despite many activities of researchers in the preparation of groundwater quality maps by using geostatistical METHODs, most of these studies have focused on the use of conventional KRIGING techniques that are not suitable for the preparation of vulnerable zones of contamination, In this study spatial variations of aquifer quality parameters and especially suitable areas for pressurized irrigation were investigated by using indicator KRIGING METHOD. For this purpose groundwater quality data from 27 wells in Birjand aquifer were studied during 2016. Qualitative parameters were evaluated including pH (acidity), EC (electrical conductivity), SAR (sodium absorption ratio), Na, NaCl, Cl and ClCO and HCO3, respectively. The allowed thresholds for use of these parameters were considered in pressurized irrigation with the proposed FAO limitaion. On the other hand, due to the importance of LangelierSaturation Index in sedimentation of drip irrigation system, this index was also evaluated for aquifer wells and unsuitable areas for pressurized irrigation in the aquifer were determined using the geostatistical indicator KRIGING METHOD and ArcGIS software. The results indicate suitable irrigation region are located in the eastern and central parts of the plain.

In mining areas, assessing toxic elements (e.g., arsenic) contamination in the soil and stream deposits is a critical issue. It is because mining activities release dangerous elements that enter the environment. In this paper, for modeling the spatial distribution of arsenic contamination in Sarduiyeh-Baft area, in Kerman Province, across an area of ca. 5000 km2, 1804 stream sediment samples were collected and analyzed. The recommended standard limit for arsenic in soil is 20 ppm, so samples showing arsenic concentration >20 ppm are contaminated samples, which need land reform processes. However, since the number of collected samples is limited, indicator KRIGING METHOD was used to identify the possibility of contamination. In the study area, there are 32 known occurrences of porphyry-Cu deposits. Thus, in order to estimate the arsenic contamination in the unsampled locations, indicator KRIGING METHOD was used. The results indicate arsenic contaminations in north and northwest parts of the study area, which could be occurred by mining of the porphyry-Cu deposits. However, the results show that there is no arsenic contamination in the eastern part although there are several mining sites with high activities.

In the present paper, to determine the pressure-dependent yield surface of polypropylene/nanoclay nanocomposites, the extended Drucker-Prager yield criterion is used and its parameters are derived by a combined experimental/numerical/optimization approach. In this METHOD, the difference between the experimental and numerical results obtained from three-point bending test is minimized. In order to alleviate the burdensome numerical simulation, a surrogate model based on KRIGING METHOD is used to estimate the cost function. The optimum of this function is obtained by maximizing expected improvement METHOD. Afterwards, the results are verified by tension and compression tests. The results show that this METHOD can substitute the complicated experimental tests which are normally employed to identify the extended Drucker-Prager parameters. Also, this METHOD can be used to determine the mechanical properties of thermoplastic material such as tensile and compressive yield stresses and elastic modulus using only a three-point bending test. In addition, it is found that the volumetric change of thermoplastic during plastic deformation is significant and the non-associative, compared with the associative, plastic flow assumption is more proper for this material for the extended Drucker-Prager criterion.