Determination of groundwater quality is an important issue in watershed management. The aim of this study was to investigate the spatial variability of some properties of Kerman groundwater using geostatistical methods. For this, groundwater quality data including soluble calcium, magnesium, sodium, bicarbonates, sulphates, pH, EC, SAR, TDS and TH for spring season of 2009 were collected. Interpolation analysis was done using Kriging, Co-Kriging, inverse distance weight (IDW) and normal distance weight (NDW) methods. The best interpolation method was chosen through root mean square error, mean absolute error and Pearson’s coefficient parameters. Result showed that the best fitted model for magnesium and pH was linear to sill, whereas for the other groundwater properties the Gaussian’s model was the best. Spatial dependency for bicarbonates and pH was moderate, while for the other ones, strong dependencies were observed. The result also indicated that for all the groundwater properties, Kriging with the Pearson’s coefficient ranging from 0.695 to 0.851 was the best algorithm. In addition to Kriging, the NDW and IDW methods found to be appropriate for the estimation of all the parameters except for EC and TDS. For these two latter ones, additional to Krging, the Co-Kriging method led to suitable estimates. The prepared maps showed that the amounts of bicarbonates and pH were higher at the north and south sides of the study area, respectively.The amounts of the other properties were minimum at the east and north east, while increased toward the west and south west.

In recent decades, the application of intelligent evolutionary methods and hybrid models for forecasting groundwater spatiotemporal fluctuations were more focused by researchers. Genetic algorithm and Neuro-Fuzzy are new methods which are applicable in single and hybrid forms for forecasting in complex and nonlinear problems. In this research, aforementioned methods were applied to study the Hadishahr plain aquifer. The Hadishahr plain is located in the north of East Azerbaijan province and it is a part of Julfa–Duzal study area. This aquifer suffers from groundwater level declination due to groundwater withdrawal increase. To achieve practical ways for spatio-temporal groundwater level forecasting, the artificial intelligence methods such as neuro–fuzzy (NF), genetic programming (GP) and combination their best model with geostatistical methods were used.Precipitation and evaporation in t0 time step and groundwater table in t0-1 time step were the inputs to the Neuro-Fuzzy and Genetic Programming. The results showed that the average RMSE of selective piezometers for genetic programming and neuro-fuzzy were 19 and 23 centimeter in the test step, respectively. Then, genetic programming was used to present a hybrid model in combination with the geostatistical model (kriging). Finally, the hybrid model-genetic - kriging‖ were applied to predict the spatiotemporal prediction of the groundwater level. The simulated results were extended to the whole plain and the area with no groundwater level monitoring network.

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.

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As natural land features, landscapes ranging in size from large plains and mountains to small hills are regarded as morphological formations characteristic of the earth's surface. The objective of the present study was to determine the physiographic unit boundaries in Hormozabad, Rafsanjan, using the ordinary kriging estimator. For this purpose, 77 observation points at distances of 500 m from each other were selected over a regular sampling grid and soil samples were collected from depths of 0– 40, 40– 80, and 80– 120 cm at the specified locations. Similar to the findings reported elsewhere and based on model efficiency estimates, the results obtained showed that ordinary kriging served as a good estimator in zoning the properties of the study area. Additionally, geostatistical results indicated that a sampling distance of 1800 m could be used as a criterion for future surveys in areas similar to the present study one. Comparison of the kriging and topographic maps of the area demonstrated that the kriging estimator used was superior to topographic maps in representing physiographic units. This was evidenced by the more accurate unit boundaries detected by the estimator despite the high variations in soil properties depicted in the respective variograms for intervals above 1800 meters. It is, therefore, recommended that modern techniques such as geostatistics should be employed for detailed soil studies as required for preparing more adequately detained maps with precise data.

The present study demonstrates a method in groundwater quality assessment using the Empirical Bayesian Kriging and Moran Spatial Autocorrelation Index. In this study, concentration of Arsenic, Lead, Iron, Manganese and Nitrate and also groundwater table and Total Dissolved Solid has been measured for 21 point in Azarshahr Plain (East Azerbaijan). Azashahr study area is one of the Lake’ s twelve adjacent aquifers that is located between 45° , 46’ to 45° , 50’ longitudinal and 37° , 43’ to 37° , 52’ latitudinal. Its total area is about 457 km2, that its plain has an area about 124 km2. The highest and lowest heights in the study area are 3700m and 1282m, respectively. Its average annual precipitation is about 221. 2 mm whereas the average annual evaporation is about 1579 mm. The most important stream in Azarshahr Plain is Azarshahrchai which has a southeast-northwest trend and is eliminated before reaching to the lake because of wide agricultural usage. On the other hand, the total annual discharge of aquifer is about 90. 64 million that is one of the groundwater depletion and decreasing the quality of groundwater factors in the study area. Moran’ s I is a commonly used indicator of spatial autocorrelation. In this study, the Moran’ s I was used as the fi rst measure of spatial autocorrelation. Its value ranges from − 1 to 1. The value “ 1” means perfect positive spatial autocorrelation (high values or low values cluster together), while “ − 1” suggests perfect negative spatial autocorrelation (a checkerboard pattern), and “ 0” implies perfect spatial randomness. After that, the appropriate weight has given to the aforementioned parameters, considering the international standard of water quality and spatial autocorrelation index of each of them. After determining the layer rules, the Expert Choice software was applied to calculate the comparing binary matrix of analytic hierarchy process. After that, the final weight for each layer with inconsistency of 0. 08 was derived that is less than 0. 1 and acceptable. In the conventional geostatistical approaches for interpolation and kriging, the variance structure is estimated first, and then the estimated variance is used for interpolation that whereas a Bayesian approach to the interpolation of spatial processes will provide a general methodology for taking into account the uncertainty about parameters on subsequent predictions. The Bayesian approach generalizes automatically to the case which the variogram parameters are unknown, whereas the classical approach essentially makes the assumption that these are known and only deals with the question of uncertainty of model parameters in a very peripheral way. Then replacing the popular interpolation methods, the Empirical Bayesian Kriging prediction method has utilized to expand every parameter to the whole plain. In order to evaluate the prediction results, the cross validation method was used. The study area was divided to 4 sections, as desirable, acceptable, moderate and non-acceptable. The final obtained map reveals that desirable quality is just located in the southeast of the study area in the upstream of the groundwater input. The acceptable quality of the groundwater is located in the east and southeast of the study area. The center, west and northwest of the study area has a moderate quality. The groundwater in the north, northwest and southwest of the study area has a non-acceptable quality that seems due to anthropogenic activities, especially agricultural and industrial during the recent years. On the other hand, the spatial autocorrelation model of effective parameters on water quality in the on hand and Bayesian kriging method with its precise assessment and prediction in some areas without data have a high applicability. The cross validation technique in model accuracy approving, is a valuable tool. Every three methods played an important role in modifying and improving the analytic hierarchy process of the groundwater quality assessment in the study area. The resulted map revealed that the groundwater quality of the east and southeast of the study area are desirable and acceptable with about 32. 53 and 44. 38 km2, respectively. The center and west section with area about 69. 32 and the north and southwest with area about 27. 21 km2 have a moderate and non-acceptable quality, respectively. Upon to the analytic hierarchy process of the groundwater quality assessment of the Azarshahr Plain, it has been revealed that 16 percent of aquifer has non-acceptable quality, about the 40 percent has moderate quality and other has an acceptable to desirable quality in the study area. The result of this study has shown the necessity of the groundwater quality precise monitoring in the study area.

This paper investigates the effectiveness of Kriging and Inverse Desistance Weighting (IDW) methods and Hotspot analysis in evaluation of groundwater quality in Kerman Plain, Iran. In order to conduct this study the information regarding the concentration level of electrical conductivity (EC), total dissolved salts (TDS) and sodium adsorption ratio (SAR) of 60 exploiting wells in the study area were obtained. The positional accuracy and correctness of this information were confirmed. Then, interpolation maps were produced for EC, TDS and SAR parameters using the raw data and the output of hotspot analysis (Z-Score) with the application of IDW and Kriging methods. This result demonstrates preferability of Kriging in regard to IDW using the output of Hotspot analysis (Z-Score), as the experts don’ t interfere with the range and number and produced maps are based on geostatistical analysis. Furthermore, the resulted maps indicated sharp boundaries of very high and very low concentration of each parameter and the calculated area for EC (1411, 1063 km2), TDS (1874, 1470 km2) and SAR (321, 396 km2) are very close to reality. Accordingly, applying the output of hotspot analysis in combination with other interpolation methods can assist researchers in determining the extent of very high and very low concentration of a parameter and in calculating the area of the affected region.

Background: Arsenic occurs naturally in the earth’ s crust. Although arsenic is useful for various industrial, agricultural, medicinal and other purposes, it exerts a toxic effect in a variety of organisms, including humans. Arsenic could be entered into the environment through anthropogenic (produced directly by human activities) and natural (weathering and leaching from rocks and mineral layers of the earth and sedimentation) sources. This study evaluates the source of arsenic contaminations in the south of Kerman state by preparing the arsenic distribution map. Materials and Methods: In south of Kerman Province, with a surface of ca. 5000 km2study, results of assaying the 1804 stream sediment samples for arsenic contamination are used for spatially modeling. Empirical Bayesian kriging (EBK) modelling has been applied to assess the prediction of arsenic contamination for other non-sampling. Results: The level of arsenic concentration is more than the standard value (12 ppm) in north-western part of the area. This contamination can cause several diseases such as skin, Bladder and Lung cancers, diabetes and Cardiovascular in these areas. Therefore it’ s necessary to evaluate the frequency of these diseases in the contaminated area. Conclusion: Comparison of contaminated area with the location of mineral deposits, mines, chemical industries and agricultural area shows that the source of this contamination could not be anthropogenic and natural source is more probable. Because the spatial modeling is prepared based on the stream sediment samples, it is necessary in the next step, to sample from soil, surface and underground water of contaminated area.

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.