Kriging Geostatistical Method Research Articles

DESARROLLO DE MÉTODOS BASADOS EN REDES NEURONALES EN LA ESTIMACIÓN DE RECURSOS MINERALES

Due to economic and physical limitations, our understanding of mineral resources in a specific area of interest is limited and fragmented. Traditionally, this problem has been solved using the Kriging geostatistical method, where the ore grade is estimated at unmeasured locations using known values of the grade at surrounding points. The advantage of this method lies in the calculation of weights through a spatial variability model known as a variogram. However, the method is imperfect, as it is based on the assumption of stationarity, aditivity, linearity and potential subjectivity in variographic modelling. This study proposes to approach the mineral resource estimation problem as a regression problem using neural networks, which are not subject to the restrictions of stationarity, aditivity, linearity and spatial modelling of geostatistics methods. Kriging and a radial basis function neural network and a multilayer perceptron have been compared using different validation metrics. The results show that a properly trained neural network model, with appropriate labelling of the mineral grade and its input characteristics, achieves similar results to the geostatistical approach, with a significant reduction in time, while avoiding all the aforementioned assumptions. However, neural networks do not consider the spatial correlation of ore grade or reproduce it at the locations where it was measured, characteristics that have marked distrust in its industrial implementation and that are discussed in this article, finally proposing an adjustment between both approaches at a minimum sacrifice of time and labor costs. Keywords Neural networks; machine learning; geostatistics.

Assessments of Gravity Data Gridding Using Various Interpolation Approaches for High-Resolution Geoid Computations

This article investigates the role of different approaches and interpolation methods in gridding terrestrial gravity anomalies. In this regard, first of all, simple and complete Bouguer anomalies are considered in gravity data gridding. In the comparison results of gridding these two Bouguer anomaly datasets, the effect of the high-frequency contribution of topographic gravitation (by means of the terrain correction) is clarified. After that, the role of the used interpolation algorithm on the resulting grid of mean gravity anomalies and hence on the geoid modeling accuracy is inspected. For this purpose, four different interpolation methods including geostatistical Kriging, nearest neighbor, inverse distance to a power (IDP), and artificial neural networks (ANNs) are applied. Here, the IDP and nearest neighbor methods represent simple-structured algorithms among the interpolation methods tested in this study. The ANN method, on the other hand, is preferred as a complex, optimization-based soft computing method that has been applied in recent years. In addition, the geostatistical Kriging method is one of the conventional methods that is mostly applied for gridding gravity data in geodesy and geophysics. The calculated gravity anomalies in grids are employed in high-resolution geoid model computations using the least squares modifications of Stokes formula with additive corrections (LSMSA) technique. The investigations are carried out using the test datasets of Auvergne, France that are provided by the International Service for the Geoid for scientific research. It is concluded that the interpolation algorithms affect the gravity gridding results and hence the geoid model determination. The ANN method does not provide superior results compared to the conventional algorithms in gravity gridding. The geoid model with 4.1 cm accuracy is computed in the test area.

Resources Estimation of Laterite Nickel Using Ordinary Kriging Method at PT Mahkota Semesta Nikelindo District Wita Pond Morowali District

Resources have economic value, form, quality, quantity, grade, geological characteristics, and certain sustainability to be extracted economically. Mineral resources decrease based on the level of geological confidence in the Inferred, Indicated, and Measured categories. This study uses the Ordinary Kriging geostatistical method to assess the potential of nickel laterite resources and the distribution of nickel mineralization levels in the study area. The research methodology was inspired by statistical and geostatistical analysis, starting with univariant statistical analysis, spatial statistics, bivariant statistics, and resource estimation. For later use in determining the distribution of mineralization grades and classifying nickel laterite resources using the Relative Kriging Standard Deviation (RKSD) calculation. This method estimates nickel content in a block whose grade value is unknown. The results of statistical calculations using Ordinary kriging obtained an average grade value of 2.90% Ni. Mineralization data for nickel content in limonite layers with Ni content of 0.5 – 1.3% and saprolite layers with Cut of Grade (COG) Ni, > 1.4 – 3.1% Ni in limonite and saprolite layers are projected in the block model. The estimated tonnage of nickel resources using the OK method is 670,837.83 tonnes. Laterite nickel resource classification results RKSD calculation are classified into measured resources (Measured).

Groundwater sustainability in the Friuli Plain

Groundwater resources in the Friuli Venezia Giulia (FVG) Region (northeast Italy) are an important natural wealth in terms of quantity, quality and ease of supply. This optimal condition, however, has long believed that it allowed an irrational and uncontrolled exploitation that inevitably produced tangible consequences on the water resources availability.The goal of the present research is the evaluation of the sustainable use of the groundwater in the Friuli Venezia Giulia Region aimed at providing guide-lines for its rational use. The study area belongs to the hydrogeological basin of the Friuli Plain and includes part of Veneto Region and Slovenia. The plain area is divided in two parts: the High Plain (HP) characterized by a phreatic aquifer and the Low Plain (LP) where eleven confined aquifer systems were identified. The two physiographic zones are separated by the spring belt. In order to evaluate the groundwater avaliability, a 3D model of the Low Friuli Plain aquifer systems was realized using Rockworks R14 software, starting from 603 lithostratigraphic wells data. Isobath and isopach maps were elaborated using kriging geostatistical method. Precipitation, evapotranspiration, runoff and infiltration were calculated to evaluate the inflow and outflow groundwater budget terms (109 rainfall and 46 thermometric stations, time series 1979-2008). To better define the river influences and the outflows at sea, a series of recent surveys on river discharges and surface withdrawals were considered. To obtain the water-budget in non-natural conditions, as the currents, were taken into account the groundwater withdrawals that were evaluated for each type of use and for each aquifer systems, starting from 2 geodatabases: one for the domestic uses (50101 estimated wells) and one for the industrial, agricultural, fish breeding, hygienic, geothermal and other minor uses (7594 wells). Well withdrawals amount were evaluated on annual base for recent periods and expressed as m3/s. The total current estimated groundwater withdrawals reach 62.4 m3/s, of which 41.1 m3/s from the confined aquifer systems in the LP area. More than 52% of the withdrawals are due to the domestic wells. For the water-budget, the studied territory was considered a “semi-closed box” in which groundwater sharings with the neighboring areas are not relevant and where the recharge is mainly due to the influent character of the river, infiltration, rainfall and irrigation practices. As result, the waterbudget for the confined LP is equal to +2.6 m3/s representing the accuracy, fairly acceptable for the scale of the research. Withdrawal entity, spring belt discharge, phreatic levels and confined aquifer’s pressure are closely interdependent and in dynamic equilibrium. The sustainability comes from the consistency and ratio between recharge and withdrawals. The confined aquifer withdrawals in the LP represent the 23.1% of the groundwater recharge coming from the HP and the 30.6% of the spring belt discharge. Seen that this last one can not further decrease, unless loss of important ecosystems, is likely to think that the present situation can be defined of limit equilibrium.

Spatial Optimization of the Groundwater Quality Monitoring Network in the Kingdom of Bahrain

Groundwater resource development, management, and planning essentially rely on their quantitative and qualitative monitoring. The information obtained from the monitoring networks of a given groundwater resource is used as a significant indicator for the status of that resource, and management schemes are subsequently made in order to develop and utilize this resource on a sustainable basis. In this study, the performance of the existing monitoring network of the quality of groundwater in Bahrain was evaluated and was spatially optimized using the geostatistical method of kriging. The estimation variance was used as a criterium in the design process, and variance reduction was used to measure the network performance. The process resulted in an increase in the number of observation points from 15 currently monitored wells to 91 wells, with 74% of these being augmented industrial and commercial wells that were to be self-reported to internalize the cost of groundwater management in their users. It is recommended that temporal optimization procedures for the groundwater level are conducted and that monitored groundwater quality data are stored in a dedicated groundwater management information system (MIS) along with the monitored data of groundwater levels and abstraction to effectively support the process of decision making for groundwater planning and management.

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions

There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding the size and representativeness of limited ground reference stations for model development, reconciling multi-source data, and interpretability of deep learning models. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network. A set of raster predictors with varying data quality and spatial scales was retrieved and processed, including gap-filled satellite aerosol optical depth products and airborne LiDAR-derived 3D urban form. We developed a multi-scale, attention-enhanced convolutional neural network model to reconcile the LCS measurements and multi-source predictors for estimating daily PM2.5 concentration at 30-m resolution. This model employs an advanced approach by using the geostatistical kriging method to generate a baseline pollution pattern and a multi-scale residual method to identify both regional patterns and localized events for high-frequency feature retention. We further used permutation tests to quantify the feature importance, which has rarely been done in DL applications in environmental science. Finally, we demonstrated one application of the model by investigating the air pollution inequality issue across and within various urbanization levels at the block group scale. Overall, this research demonstrates the potential of geospatial AI analysis to provide actionable solutions for addressing critical environmental issues.

Quantification of geodetic strain rate uncertainties and implications for seismic hazard estimates

SUMMARYGeodetic velocity data provide first-order constraints on crustal surface strain rates, which in turn are linked to seismic hazard. Estimating the 2-D surface strain tensor everywhere requires knowledge of the surface velocity field everywhere, while geodetic data such as Global Navigation Satellite System (GNSS) only have spatially scattered measurements on the surface of the Earth. To use these data to estimate strain rates, some type of interpolation is required. In this study, we review methodologies for strain rate estimation and compare a suite of methods, including a new implementation based on the geostatistical method of kriging, to compare variation between methods with uncertainty based on one method. We estimate the velocity field and calculate strain rates in southern California using a GNSS velocity field and five different interpolation methods to understand the sources of variability in inferred strain rates. Uncertainty related to data noise and station spacing (aleatoric uncertainty) is minimal where station spacing is dense and maximum far from observations. Differences between methods, related to epistemic uncertainty, are usually highest in areas of high strain rate due to differences in how gradients in the velocity field are handled by different interpolation methods. Parameter choices, unsurprisingly, have a strong influence on strain rate field, and we propose the traditional L-curve approach as one method for quantifying the inherent trade-off between fit to the data and models that are reflective of tectonic strain rates. Doing so, we find total variability between five representative strain rate models to be roughly 40 per cent, a much lower value than roughly 100 per cent that was found in previous studies (Hearn et al.). Using multiple methods to tune parameters and calculate strain rates provides a better understanding of the range of acceptable models for a given velocity field. Finally, we present an open-source Python package (Materna et al.) for calculating strain rates, Strain_2D, which allows for the same data and model grid to be used in multiple strain rate methods, can be extended with other methods from the community, and provides an interface for comparing strain rate models, calculating statistics and estimating strain rate uncertainty for a given GNSS data set.

Optimization of saturated hydraulic conductivity estimation using kriging in drainage networks

Statistical analysis of saturated hydraulic conductivity is the fundamental of engineering plans for water resource projects. In the other words, project solutions and designs depend directly on the accuracy of these statistical analyses and the methods that develop them to studied area. One of the recent methods was used to estimate the variables such as hydraulic conductivity is Kriging method. In this study, GIS software was used for this purpose and kriging method was used to estimate the saturated hydraulic conductivity of the soil. Spherical model with nugget effect of 0.0039 and sill of 0.0262 was the best variogram for determining soil hydraulic conductivity in this method, which expressed the high strength of regional variable structure in the studied area. The results showed that the simple kriging method with a regression coefficient of 0.82 has the highest accuracy in modeling the saturated hydraulic conductivity function. Also, the results showed that the geostatistical kriging method for modeling soil hydraulic conductivity index has a higher accuracy in determining saturated soil hydraulic conductivity than the traditional Thiessen method.

XCO2 and XCH4 Reconstruction Using GOSAT Satellite Data Based on EOF-Algorithm

The Greenhouse Gases Observing Satellite (GOSAT) can help to ascertain the global distribution of carbon dioxide (CO2) and methane (CH4), and how the sources and sinks of these gases vary by season, year, and location. However, the data provided by the GOSAT level 2 and 3 products have certain limitations due to their lack of spatial and temporal information; even with the application of the kriging geostatistical method on the level 2 products, the processing algorithms still need further upgrades. In this study, we apply an empirical orthogonal function (EOF)-based method on the GOSAT L3 products (137 images, from January 2010 to May 2021) to estimate the column average of carbon dioxide and methane (XCO2–XCH4) within the entire Earth. The reconstructed results are validated against the Total Carbon Column Observing Network (i.e., TCCON), with 31 in situ stations, and GOSAT L4B column-averaged data, using 107 layers. The results show an excellent agreement with the TCCON data and exhibit an R-squared coefficient of 0.95 regarding the CO2 measurements and 0.86 regarding the CH4 measurements. Therefore, this methodology can be incorporated into the processing steps used to map global greenhouse gases.

A novel artificial intelligence approach for regolith geochemical grade prediction using multivariate adaptive regression splines

• Multivariate Adaptive Regression Spline (MARS) is proposed. • The proposed MARS approach was used to predict regolith geochemical grade. • Three other artificial intelligence techniques and kriging were compared with MARS. • The MARS technique gave superior results. The necessity for applying a potent analytical regolith geochemical grade estimator is driven by the reality of mineral exploration. This is because many exploration geologists rely upon the classical geostatistical method of Kriging which oftentimes do not produce accurate predictions due to the complexity of interactions between geological features and spatial variables. In this study, a novel non-linear data-driven approach known as Multivariate Adaptive Regression Spline (MARS) is proposed as an effective predictive tool to unravel regolith geochemical complexities. The proposed MARS approach was used to predict regolith geochemical grade from a thick regolith cover in the Tarkwaian paleo-placer of the South-Western Ashanti belt in Ghana. Out of the 891 samples, the data was partitioned into 70% training (model development) and 30% testing (model validation). The proposed MARS result was compared with three other artificial intelligence techniques (i.e., radial basis function neural network, backpropagation neural network and generalised regression neural network) and kriging geostatistical technique. Based on the test results, MARS had the highest correlation coefficient ( R = 0.9675) and the least statistical error metrics (RMSE = 0.7791, MAE = 0.6014, and ρ = 0.0472). The implementation of the MARS approach in regolith geochemical grade estimation domain has yielded outstanding and promising results. The MARS superiority was evident in its calibration strength, prediction accuracy, robust interaction of variables and overcoming the black box system of ANN. Thus, the proposed MARS approach could be an excellent tool in regolith geochemical grade estimation workflow when fully integrated with exploration tasks.

Spatial variability of soil physical properties in longitudinal profiles.

The spatial variability of physical properties, such as bulk density, penetration resistance and gravimetric moisture, obtained by applying geostatistics in precision agriculture, can effectively indicate the physical behavior of agricultural soils in longitudinal profiles. In this way, the spatial dependence of physical properties in streets of coffee plantations with different lengths was evaluated in the southern Minas Gerais, Brazil. For this purpose, five longitudinal profiles were measured in streets, each one with depths ranging from 0 to 0.60 m, in six layers of 0.10 m, being the database composed of 432 property, 144 by property, submitted to the ordinary kriging geostatistical method in order to obtain spatial variability maps using the R software. They were evaluated by the lower mean cross-validation error of theoretical models fitted by ordinary least squares (OLS), being detected in higher superficial layers, from 0 to 0.30 m, lower bulk density and lower penetration resistance, with variable gravimetric moisture in the length direction of some streets of coffee plantations, being that these properties presented different structures of spatial dependence for each street.

Penerapan Geostatistika Ordinary Kriging dalam Estimasi Harga Tanah (Studi Kasus: Kecamatan Samarinda Utara Kota Samarinda)

Abstract
 Ordinary kriging geostatistical method is an interpolation method that used spatial data. This method generally used to estimate coal and mineral resources. However, over time this method is also used in estimating land prices. The purpose of this research is to determine the best model for land price estimation in North Samarinda District, especially in Sempaja Selatan Sub-District between spherical models, Exponential models and Gaussian models and to determine land price estimation using ordinary kriging geostatistical method based on the best semivariogram model displayed in the form of a contour map. The estimation process begins by create an experimental semivariogram and a structural analysis which is carried out to obtain sill, nugget and range. Then, interpolated using the three models to get the predicted value and calculated RMSE to determine the performance of the model, which the spherical model is the best model because it has the smallest RMSE value. From this method, a contour map of the estimation results of land prices in Sempaja Selatan Sub-District of North Samarinda District is obtained. The lowest land prices are found to be predominantly located in the northwest and north of Sempaja Selatan Sub-District. The lowest price of the land range from Rp. 279,000,- to Rp. 900,000,- per m2 and the highest price of land is predominantly located in the southern part of Sempaja Selatan Sub-District with prices ranging from Rp. 2,982,000, - to Rp. 4,981,000, - per m2. The variation in land prices in Sempaja Selatan Sub-District of North Samarinda District is influenced by physical factors, namely the elevation and slope of the slope. Low land prices tend to be in areas of high elevation and steep slopes, while high land prices are at low elevations and with a slope that is flat. So from this, it can be said that the land price is inversely proportional to the physical factor of the land.

The assessment of annual rainfall field by applying different interpolation methods in the state of Rio Grande do Sul, Brazil

An accurate analysis of spatial rainfall distribution is of great importance for managing watershed water resources, in addition to giving support to meteorological studies and agricultural planning. This work compares the performance of two interpolation methods: Inverse distance weighted (IDW) and Kriging, in the analysis of annual rainfall spatial distribution. We use annual rainfall data for the state of Rio Grande do Sul (Brazil) from 1961 to 2017. To determine which proportion of the sample results in more accurate rainfall distribution maps, we use a certain amount of points close to the estimated point. We use mean squared error (MSE), coefficient of determination (R2), root mean squared error (RMSE) and modified Willmott's concordance index (md). We conduct random fields simulations study, and the performance of the geostatistics and classic methods for the exposed case was evaluated in terms of precision and accuracy obtained by Monte Carlo simulation to support the results. The results indicate that the co-ordinary Kriging interpolator showed better goodness of fit, assuming altitude as a covariate. We concluded that the geostatistical method of Kriging using nine closer points (50% of nearest neighbors) was the one that better represented annual rainfall spatial distribution in the state of Rio Grande do Sul.

Effects of heavy metal contamination released by petrochemical plants on marine life and water quality of coastal areas.

The present study attempts to assess the threat of water contamination in Persian Gulf by heavy metals (Fe, Cr, Pb, Cu, Zn, Cd, Se, and Ni) and its subsequent effects on five fish species including Scomberomorus guttatus (S. guttatus), Lethrinus nebulosus (L. nebulosus), Brachirus orientalis (B. orientalis), Pomadasys kaakan (P. kaakan), and Scomberomorus commerson (S. commerson). Water and fish samples from fourteen monitoring stations were obtained to determine the concentrations of contaminants in water and fish. Heavy metal pollution index (HMPI) and non-carcinogenic hazard quotient (NHQI) were employed to evaluate contamination level in water and fish muscle. The Kriging geostatistical method was employed to determine the spatial distribution of different heavy metals around petrochemical plants. The highest NHQI values for P. kaakan and B. orientalis species were 1.036 and 1.046, respectively. In both cases, the NHQI values were higher than the maximum allowable value of 1, indicating that both fish species were on the verge of contamination by heavy metals, which in turn makes the consumption of these fish highly hazardous to human health. The lowest NHQI value was observed in S. commerson species at a value of 0.394, indicating its harmlessness to human health. Overall, fish species living within the top 5 m of the water column (S. commerson and S. guttatus) were found to be less contaminated by heavy metals compared to species dwelling near the seafloor (P. kaakan and B. orientalis). Results also indicated the pollution absorption rate in S. commerson and S. guttatus were 0.45 and 3.4 mg/kg-year, while the corresponding values for the B. orientalis and L. nebulosus species were 6 and 2 mg/kg-year, respectively. P. kaakan species showed a pollution absorption rate of 3.2 mg/kg-year. High heavy metal concentrations of 4.8, 10, 9.8, 5.2, 9.4, and 6.7 mg/L were obtained for Cr, Zn, Pb, Ni, Fe, Cu, and Cd, respectively, in water samples obtained from stations nearby petrochemical plants. The HMPI index for the most contaminated stations was ten times that of the maximum allowable limit. Given the intense activity of oil, gas, and petrochemical plants in the Persian Gulf, defining safe fishing areas by management practices similar to contamination zoning maps presented in this study can substantially protect the public health from heavy metal contamination.

Pedometric mapping for soil fertility management – A case study

Information about soil physico-chemical parameters plays an important role in precision farming. To examine the relationship among soil properties, pedometric mapping is essential and has been widely applied in agricultural activities. This paper is aimed at soil nutrient assessment by generating the geospatial distribution maps for Kestur, a village in Tumakuru district of Karnataka State, India. Using random sampling technique, a total of one hundred and sixty geo-coordinated surface (0–20 cm) soil samples were collected from different land cover regions including irrigated and dryland areas in Kestur village. The samples collected were analysed for pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), phosphorus (P as P 2 O 5 ), and potassium (K as K 2 O). Also, a structured questionnaire was used to collect data about demographic profile, farming practices, crops grown and fertilizers applied from each farmer under the study. The spatial distribution maps were generated for each soil property and were mapped using spatial variability by adopting Ordinary Kriging geostatistical method. Semivariogram models such as exponential for pH, spherical model for EC and OC and Gaussian for N, P 2 O 5 , and K 2 O were used based on the best-fit selection using least Mean Error (ME) and Root Mean Square Error (RMSE) values. The outcome of the study proved that the geostatistical methods can be utilized to develop the spatial distribution maps thereby resulting in a cost effective solution to soil fertility management.

Spatio-temporal analysis of dry and wet periods in Iran by using Global Precipitation Climatology Center-Drought Index (GPCC-DI)

In this study, the temporal and spatial characteristics of droughts in Iran, including severity, duration, frequency, and extent, were studied using the GPCC-DI, in a 68-year period from 1952 to 2019. To display and analyze these features, time trend components, intensity, duration, and area diagrams, Mann-Kendall non-parametric test and Kriging geostatistical method were used. The result of plotting intensity-duration-frequency maps shows an increasing trend in regional drought severity and spread. It was also found that the most severe droughts occurred in the 12- and 24-month periods from 2000 onward. Mann-Kendall test results also show that significant increases in droughts occurred in the central desert basins of Hamoon basin and south Baluchistan in the southeastern Iran. In addition, decrease in occurrences of dry and wet periods happened in the western, northwestern, and coastal regions of the Caspian Sea. The pattern of spatial variations of drought severity indicated two major drought foci in southeast and central Iran. The number of drought centers in Iran has also increased over time. The results of the analysis of the time trends of dry and wet periods point to the occurrence of continuous droughts in 12- and 24-month periods.

Comparison of work environment air quality and application of geostatistic technique in the spatial distribution of PM2.5 and PM10 in a number of industrial workshops

Introduction: Air quality in industrial workshops is affected by various harmful chemical agents. Purpose of this study was to compare work environment air quality in a number of industrial workshops based on the mass concentration and number of suspended particles in air and application of Kriging geostatistical method in industries.
 Materials and Methods: This observational study was carried out in four workshops including cuprous waste separation works, rolling and cutting, casting and copper smelting, manufacturing aluminum products and double-glazed glass production. Using particle mass counter sampler of Tes 5200 model, mass concentration and number concentration of suspended particles in row of different diameters were measured. The spatial distribution map of PM2.5 and PM10 in each of workshops was prepared using Kriging geostatistic method in GIS environment Version 10.5.
 Results: The highest mass concentration (8.42 mg/m3) and number concentration (445058 Particle/lit in differential mode and 885446 Particle/lit in cumulative mode) were measured in the manufacturing aluminum products workshop. The highest mass concentration and number concentration of suspended particles were obtained for row diameters of 10 µm and 0.5 µm, respectively. Interpretation of Kriging maps showed that various areas of the industries such as radiation furnace, scrap press, aluminum smelter, sieve, diamond machine, glass furnace and ingot molds had the highest frequency of PM2.5 and PM10.
 Conclusion: Engineering control actions such as installation of ventilation systems in halls and design and installation of a local hood in the entrance sections of the aluminum smelting furnace and sieve in manufacture of aluminum products workshop and copper ingot molds in casting and smelting workshop is essential for improved air quality.

ANALISIS GEOSTATISTIK UNTUK PEMETAAN PERUBAHAN KUALITAS AIR TANAH KAWASAN KARST KABUPATEN GUNUNGKIDUL

Gunungkidul Regency has a karst area of approximately 807 km2 or 53% of the total area of its territory. There is a tendency for expansion in karst mining leading to a number of potentials, including damage to the water system which is a pollution of karst water sources. Temperature, turbidity, Total Dissolve Solid (TDS), PH, hardness, manganese, iron, and chloride are parameters affecting groundwater quality. Measurement of the concentration of each parameter is performed through a long process and expensive costs. Therefore, not all measurements are performed in the entire area of Gunungkidul. Hence, it is important to interpolate the eight parameters using the geostatistical method. Geostatistical kriging method is an estimation method that reduces the error of variance estimation by a cross-correlation between primary and secondary variables. The best semivariogram for a five-year period with the smallest RMSE value is the temperature in 2018 using an gaussian model, turbidity in 2018 using a IDW model, Total Dissolve Solid (TDS) in 2017 using a gaussian model, PH in 2016 using a linear exponential, hardness in 2019 using a exponential model, manganese in 2017 using a circular model, iron in 2017 using a exponential model, and chlorides in 2015 using a RBF. Monitoring points of groundwater quality using these eight parameters have different variances so that five parameters are producing more than one RMSE value. To resolve this, besides comparing several interpolation methods, natural logarithmic transformations and the correlation of actual values with estimates were also performed. The correlation between the actual value and the estimation indicates that the estimation produced by the non-transformed data is more accurate than the transformed data. The estimated results of each parameter are visualized in the form of a map so that changes in groundwater quality every year can be seen. Besides the maps, the results of this study are shown in graphs of changes in the form of cross-sections of each parameter from 2015 to 2019. Visualization of changes in the quality level groundwater is expected to give input for relevant agencies in the conservation of water resources. Keywords: Karst Mining, Mapping, Geostatistics, Groundwater Quality.

Study of speciation and spatial variation of pollutants in Anzali Wetland (Iran) using linear regression, Kriging and multivariate analysis.

Multivariate statistical techniques and geostatistical methods are among the important tools used in surface water quality management. They are widely used in interpreting data, identifying the pollution sources, understanding the spatial variation of parameters, and determining the places of monitoring stations. Therefore, in this study, spatial variation of water quality and pollutants in the Anzali Wetland water (Iran) was evaluated using multivariate statistical and Kriging methods. The values of different water quality parameters measured in six stations in the wetland water were subjected to cluster analysis (CA) and principal component analysis (PCA). Cluster analysis reduced the number of stations from six to four. The results of PCA showed that industrial and agricultural pollution sources could be responsible for the Anzali Wetland water quality. Then, the spatial variation maps of the PCA scores were generated using Kriging geostatistical method in the geographical information system (GIS) to investigate the pollution sources affecting the wetland parts. These maps illustrated that a great part of the wetland body was under the effect of agricultural sources, while the industrial sources affected the outlet and central parts. Finally, a comparison between two models (multiple linear regression (MLR) and Kriging) was made to assess their ability in predicting water quality parameters in the study area. The results showed the improvement of prediction using MLR, which was by 25%-97%, compared with Kriging. The results of the present study can be effectively used in the planning and implementation of future monitoring networks in the Anzali Wetland and other similar aquatic systems.

SOIL SALINITY MONITORING UNDER DIFFERENT IRRIGATION TYPES, IN SINNURIS DISTRICT, FAYOUM GOVERNORATE, EGYPT

Soil salinity is one of the main factors limiting crop production in arid and semi-arid regions of the planet. Therefore, mapping and monitoring salinity concentrations is essential for land and water resources management and consequently for the improvement of agricultural production. The current study utilized the geostatistical Kriging method for soil data interpolation to generate soil salinity maps. In order to assess and monitor salinity changes in the study area under different irrigation types, the soil samples collected in 2018 and the soil salinity data available for 2009 were used. The results showed significant improvements in soil salinity levels, as the area of none saline soils (< 2 dS m-1) increased from 1.3 ha in 2009 to 9119 ha in 2018. These impressive results could be related to the construction of sub-surface drainage systems that started in 2007, consequently, demonstrate the effectiveness of water management policies in Sinnuris District. Moreover, soil salinity values showed a negative correlation with the elevation data, which could explain the increasing salinity levels around the Lake Qarun due to the shallow saline water table and the low efficiency of the drainage system.