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Monitoring groundwater levels in areas experiencing depletion is crucial for effective resource management. This study combines two approaches for estimating groundwater levels in regions lacking sufficient data for better spatial distribution estimates. To achieve this, several Artificial Intelligence (AI) models with different input features were developed using monthly groundwater level data from 2010 to 2023 in the Sacramento Valley, California. The results indicated that the Random Forest (RF) and Gradient Boosting Regressor (GBR) models, with Root Mean Square Error (RMSE) of 7.03 m and 7.83 m in the testing phase, respectively, were the most accurate. Subsequently, the data for each year in 2010–2023 were interpolated using the Ordinary Kriging (OK) method. The outputs of this method and the outputs from RF and GBR models were then merged using Bayesian Model Averaging (BMA). For 2010, 2015, 2020, and 2023, this approach reduced groundwater level estimation errors by 31.18, 41.87, 50.60, and 45.04%, respectively. Additionally, the results showed that the integrating method could reduce groundwater level estimates’ RMSE and Mean Absolute Error (MAE) by an average of 41.12 and 33.72% over 2010–2023. [ABSTRACT FROM AUTHOR]

Background: Borrelia miyamotoi and Borrelia burgdorferi sensu lato (s.l.) are important zoonotic agents transmitted by Ixodes ricinus ticks, which are widely distributed across Central Europe. Understanding the spatial distribution of these pathogens' prevalence will help identify areas with increased infection risk and facilitate the implementation of effective preventive measures. Methods: We analysed 12,955 I. ricinus ticks collected from 142 towns in the Czech Republic between 2016 and 2018. The ticks were pooled into 2591 groups of five and tested using duplex quantitative polymerase chain reaction (qPCR) for the presence of B. burgdorferi s.l. and B. miyamotoi. For each location, we estimated the overall prevalence of both agents using the EpiTools Epidemiological Calculator for pooled samples and calculated the minimum infection rate (MIR). To assess the potential risk of infection, we combined data on the abundance of nymphs and females with pathogen prevalence at each sampled site. Using a geographic information system (GIS), we mapped the MIR and infection risk of both Borrelia species across all 142 sampled locations and employed a geostatistical method (ordinary kriging) to predict MIR values and infection risk as continuous surfaces across the entire country. Results: We detected B. miyamotoi in 110 localities and B. burgdorferi s.l. in all 142 localities. The estimated prevalence of B. miyamotoi and B. burgdorferi s.l. in the collected ticks was 2.1% (95% confidence interval [CI] 1.8–2.3) and 27.1% (95% CI 26.0–28.3), respectively. For B. miyamotoi, we identified previously unknown, geographically distinct hotspots of MIR up to 8.3%, with MIR slightly higher in females (2.3%) than in males (1.9%) and nymphs (1.8%), though the difference was not statistically significant. In contrast, B. burgdorferi s.l. exhibited ubiquitous presence, with consistently high prevalence nationwide, showing similar MIRs in females (16.2%) and males (16.1%), and slightly lower in nymphs (15.6%). The highest infection risk for B. miyamotoi was 12.4 infected vectors per hour in southeastern Moravia, while the highest risk for B. burgdorferi s.l. reached 78.6 infected vectors per hour in the Bohemian-Moravian Highlands. Conclusions: Borrelia miyamotoi is widespread, forming distinct high-prevalence areas in certain regions. Borrelia burgdorferi s.l. demonstrates consistently high prevalence across most of the country, except for a few localized areas such as southwestern Czechia. Both pathogens exhibit natural nidality, forming regions with elevated prevalence and infection risk. Long-term time-series data are needed to confirm the spatio-temporal stability of these hotspots. [ABSTRACT FROM AUTHOR]

Abstract The release rate of CO2 gas can be influenced by peatlands’ physical properties, such as water level and soil moisture, and rainfall. To anticipate the unstable condition which is when the peatland emit more carbon, we developed the Generalized Space Time Autoregressive (GSTAR) model in predicting these physical properties for the following weeks. As the innovation in modelling, the spatial weight matrix was based on three-dimensional coordinates with a modification on the height factor. The data we used are real-time data of water level on the peatlands in Pulang Pisau Regency, Central Kalimantan Province from 20 February 2021 to 18 March 2023. We then used Ordinary Kriging interpolation on the prediction results to create contour maps on different dates. There were empty data on several dates, especially from 24 March until 3 August 2022. To fill the empty data, we used linear interpolation and then we added white noise to the interpolation results. From the data, the water level has a downward trend pattern from around November to September and an upward trend pattern from October to November. Furthermore, we found that the best model for water level was GSTAR (2;0.1) with a modified matrix $$a=0.1$$ a = 0.1 and $$b=1.1$$ b = 1.1 . Based on the predicted water level, there is a risk of changes in the properties of the peatlands in several areas in Pulang Pisau Regency.

Compared to inland areas, meteorological stations in the island regions appear scarce and unevenly distributed, which leads to noteworthy uncertainty in detailed characterisation of various meteorological elements. For the Zhoushan Islands, located in Southeast China, there exist many islands and islets, and the local terrain is quite complex. Therefore, different interpolation strategies usually generate diverse gridded results, which largely influence the reliability and accuracy of operational climate monitoring and diagnosing. Under the background of climate change, the Zhoushan region is frequently invaded by cold waves in recent years, so how to scientifically choose an interpolation scheme to reasonably represent spatial distribution characteristics of temperature becomes an important issue in local climate operations. To solve this problem, based on the index of root mean square error (RMSE), the interpolation effect of Ordinary Kriging (OK), Inverse Distance Weighting (IDW), and ANUSPLIN (ANU) are comparatively analysed for 8 cold wave processes influencing Zhoushan during 2014-2021. Two subdivided indices, i.e., temporal RMSE (TRMSE) and spatial RMSE (SRMSE) are further designed to evaluate the interpolation results on temporal and spatial dimensions respectively. Eleven stations are randomly selected from the total 53 meteorological observational stations to test the interpolation results of OK, IDW, and ANU for the minimum temperature, reduction of daily minimum temperature and daily-mean temperature in the 8 processes. It can be found that the bias in the ANU case is higher than that in the OK and IDW cases. To explain such a phenomenon, 3 interpolation experiments with dense surrounding stations, sparse surrounding stations, and specific distribution of examining stations (all the examining stations are not distributed in the main island of Zhoushan) are further designed. The results demonstrate that the performance of the ANU strategy is closely linked to the spread situation of peripheral stations. When the surrounding stations are concentrated, the interpolation bias of ANU is usually smaller than that of OK and IDW. However, if the surrounding stations appear sparse, the bias of ANU exhibits much larger. In the scenario of dense peripheral stations, regardless of the examining sites distributed over the main island or not, the ANU solution can always get the optimal interpolation results, which implies that the impact of topography on the performance of ANU in temperature interpolation is of less importance. Also, the influence of horizontal resolution for interpolation seems secondary. When the horizontal resolution for three interpolation schemes falls down to 1 km×1 km from 30 m×30 m, the change of RMSE is generally less than 0.1 ℃ for most circumstances, so the impact of interpolation resolution can be neglected.

To consider the complex soil-structure interaction in a pile-slope system, it is necessary to analyze the performance of pile-slope systems based on a three-dimensional (3D) numerical model. Reliability analysis of a pile-slope system based on 3D numerical modeling is very challenging because it is computationally expensive and the performance function of the pile failure mode is only defined in the safe domain of soil stability. In this paper, an efficient hybrid response surface method is suggested to study the system reliability of pile-reinforced slopes, where the support vector machine and the Kriging model are used to approximate performance functions of soil failure and pile failure, respectively. The versatility of the suggested method is illustrated in detail with an example. For the example examined in this paper, it is found that the pile failure can significantly contribute to system failure, and the reinforcement ratio can effectively reduce the probability of pile failure. There exists a critical reinforcement ratio beyond which the system failure probability is not sensitive to the reinforcement ratio. The pile spacing affects both the probabilities of soil failure and pile failure of the pile-reinforced slope. There exists an optimal location and an optimal length for the stabilizing piles.

The release rate of CO2 gas can be influenced by peatlands' physical properties, such as water level and soil moisture, and rainfall. To anticipate the unstable condition which is when the peatland emit more carbon, we developed the Generalized Space Time Autoregressive (GSTAR) model in predicting these physical properties for the following weeks. As the innovation in modelling, the spatial weight matrix was based on three-dimensional coordinates with a modification on the height factor. The data we used are real-time data of water level on the peatlands in Pulang Pisau Regency, Central Kalimantan Province from 20 February 2021 to 18 March 2023. We then used Ordinary Kriging interpolation on the prediction results to create contour maps on different dates. There were empty data on several dates, especially from 24 March until 3 August 2022. To fill the empty data, we used linear interpolation and then we added white noise to the interpolation results. From the data, the water level has a downward trend pattern from around November to September and an upward trend pattern from October to November. Furthermore, we found that the best model for water level was GSTAR (2;0.1) with a modified matrix a = 0.1 and b = 1.1 . Based on the predicted water level, there is a risk of changes in the properties of the peatlands in several areas in Pulang Pisau Regency. [ABSTRACT FROM AUTHOR]

The growth of a city is typically accompanied by densification and sprawl, the former through verticalization, urban renewal, and the filling in of empty spaces. All of these activities extend and intensify the urban heat island (UHI), which is quantified in this study as the difference in daily minimum temperature between urban and rural areas. Here, we investigate this phenomenon in the area of Rennes (France) and 17 surrounding cities using the Rennes Urban Network which comprises 93 weather stations. This study aims to 1) determine the optimal method for spatializing UHI in Rennes, France, 2) estimate and spatialize the UHI in the small peri-urban cities surrounding Rennes. For this, we model mean UHI and intense UHI using three methods of interpolation--multi-linear regression (MLR), ordinary kriging (OK), and regression kriging (RK)--based on data from 2022. We find that the RK method is the most suitable overall, with an RMSE of 0.11°C for mean UHI and 0.25°C for intense UHI. This approach allows stochasticity to be taken into account, and thus provides a better representation of UHI variation within Rennes and its peri-urban cities. [ABSTRACT FROM AUTHOR]

Extraction of plastic particles from soil is challenging and, thus, exceptionally little spatial information on plastic distribution at the field scale has been gathered. However, for environmental risk assessment, adequate sampling should complement coherent plastic profiling. In this study, we investigated the spatial distribution of mesoplastics (MePs; from >5 mm up to 130 mm) in arable soil (Haplic Cambisol) managed intensively by 12 years of compost application. Geo‐referenced samples (n = 128) and five different sampling designs (n = 45) of variable sampling volume (from 2 to 300 L) were collected at a three hectare study site in Northern Germany (0–30 cm soil depth). Soil properties such as pH and soil organic carbon (SOC) were measured to evaluate dispersion measures of these data. In total, we found 259 MePs with a predominance of transparent packaging foils made of polyethylene and coloured fibres of polypropylene. Average particle metrics were a projection area of 47 (3–400) mm2, a Feret diameter of 18.5 (5.4–130) mm and a mass of 1.89 (0.11–221) mg. Caution is advised when measuring the particle mass due to still strongly adhering soil material, especially for fibre bundles with 0.544 mg soil mg−1 particle. We recommend using a 0.1 mol L−1 tetrasodium pyrophosphate solution to purify MePs by removing attached soil before weighing for further environmental risk assessment. The MePs count with a median value of 0.50 (0–3.2) particles kg−1 and median mass of 2.26 (0–221) mg kg−1 featured the highest coefficient of variation (CV) with 103% and 187%, respectively. This is 10–20 times larger in comparison to the CV of SOC (9.2%) and even 50–93 times larger than CV of soil pH (2.2%). This leads to the need of larger sample numbers to delineate plastic metrics in comparison with soil properties to identify a reliable mean value of the field within a predefined allowable error. Mesoplastics in the soil were characterized by a pure nugget effect variogram (no spatial correlation), revealed no intrafield variability and the sample volume yielded inconclusive results. Sampling for plastics in soil should either (i) drastically increase the sample number for a single field or (ii) communicate transparently that the allowable error is by far enhanced in comparison with classical soil properties like pH and SOC. More systematic studies featuring geo‐spatial analysis of MePs and smaller‐sized plastics in soils are required to propose adequate sampling designs across multiple land uses and plastics fingerprints. A larger database would, thereupon, pave the way for best‐practice guides on how to treat 'outliers' and search for robust estimators for spatial mapping of plastics in soils. [ABSTRACT FROM AUTHOR]

Recently, spaceborne LiDAR data have been increasingly utilized for large-scale forest aboveground biomass (AGB) estimation. However, due to the discontinuity of laser spot sampling points, it is often necessary to integrate auxiliary data to estimate the continuous distribution of forest AGB, resulting in uncertainties in the estimation method. In this study, we focus on the Qilian Mountains National Park in northwestern China as a sample area. Three forest AGB estimation models (Ordinary Kriging (OK), Support Vector Regression (SVR), and Random Forest (RF)) were developed based on nonparametric algorithms by integrating ICESat/GLAS data, Landsat OLI imagery, and field inventory data. The accuracy of the estimation results generated by these three models was independently verified using forest inventory data. The results indicated that the root mean square error (RMSE) of the three models, from low to high, were SVR(19.053 t·hm-2), RF(21.074 t·hm-2), and OK(26.362 t·hm-2). The mean relative error (MRE), from low to high, were SVR (31.890%), RF (33.314%) and OK (55.398%). Moreover, except for the OK model, the total relative error (TRE) of the SVR and RF models falls within an acceptable range. Further verification of the accuracy of the spatial distribution of forest AGB was conducted for the SVR and RF model. It was found that the forest AGB spatial distribution generated by the SVR model was closer to the forest resource inventory data than that generated by the RF model. Therefore, we conclude that the SVR forest AGB estimation model demonstrates better quantitative accuracy and distribution accuracy. We also anticipate that these results can serve as a reference for forest AGB estimation based on spaceborne LiDAR data in future studies. [ABSTRACT FROM AUTHOR]

The atmospheric phase, which is the sum of vertical stratification and turbulent atmospheric phase, is a major challenge currently faced by small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) measurements. Many previous studies have demonstrated that the former can be separated from the interferogram by establishing a functional model between it and the topography. Due to the high variability of the turbulent atmospheric phase (TAP) in the space and time domains, however, the TAP is difficult to model and remove. Recently, many stochastic models have been developed to reduce the influence of the TAP in SBAS-InSAR. To avoid the rank deficient in stochastic model method, we present a correction method using network-based variance estimation, interferogram stacking and ordinary kriging interpolation (NIO). There are three main steps in the proposed algorithm to ensure the accuracy of the correction result: (1) adaptively identify and select sufficient good-quality interferograms that contain less turbulent atmospheric noise to participate in deformation calculation; (2) further select the short temporal baseline interferogram and mask the corresponding deformation location to avoid the effect of deformation; and 3) take advantage of ordinary kriging interpolation to reduce the effects of TAP from the selected good-quality interferograms. The performance of the proposed method has been validated with a set of simulations and real Sentinel-1A SAR data in Southern California, USA. [ABSTRACT FROM AUTHOR]

Quantification of soil water-physical properties and their spatial variation is important to better predict soil structure and functioning, as well as associated spatial patterns in the vegetation. The provision of site-specific soil data further facilitates the implementation of enhanced land use and management practices. Using geostatistical methods, this study quantified the spatial distribution of soil bulk density (SBD), soil moisture (SM), capillary water-holding capacity (CWHC), capillary porosity (CP), non-capillary porosity (NCP), and total porosity (TP) in southern subtropical forests located at the Tropical Forest Research Center in Pingxiang City, China. A topographic map (scale = 1:10,000) was used to create a grid of l km squares across the study area. At the intersections of the grid squares, the described soil water-physical properties were measured. By calculating the coefficient of variation for each soil water-physical property, all measured soil water-physical properties were found to show moderate spatial heterogeneity. Exponential, gaussian, spherical, and linear models were used to fit the semivariograms of the measured soil water-physical properties. Across all soil water-physical properties, the range A0 variable (i.e., the separation distance between the semivariance and the sill value) measured between 3419 m and 14,156 m. The nugget-to-sill ratio ranged from 9 to 41%, indicating variations in the level of spatial autocorrelation among the soil water-physical properties. Many of the soil water-physical properties were strongly correlated (as assessed using Pearson correlation coefficients). Spatial distribution maps of the soil water-physical properties created via ordinary kriging (OK) showed that most water-physical properties had clumped (aggregated) distributions. SBD showed the opposite spatial pattern to SM and CWHC. Meanwhile, CP and TP showed similar distributions. [ABSTRACT FROM AUTHOR]

The Yellow River Delta (YRD) has the world's highest land formation rate. However, soil salinization has caused severe land degradation in the region. Understanding the distribution of soil salinity and its variation is essential for saline soil management. This study combined soil salinity sampling data, remote sensing imagery, and layers of geographic environmental factors. Three different models, including Ordinary Kriging (OK), Geographically Weighted Regression (GWR), and Bayesian Maximum Entropy (BME), were used and compared to predict the soil salinity of two soil layers in 2006 and 2022. Then the spatial distribution characteristics and development trends of soil salinity in the YRD were analyzed. The results indicated that (1) The BME model is an optimal salinity prediction model that integrates soft data from multiple sources to perform nonlinear estimates. Compared to the OK and GWR models, the RMSE was reduced by up to 25% and 13%, respectively, and the greatest improvement in R2 was increased from 0.0534 and 0.2718 to 0.5569, respectively. (2) Soil salinity in the YRD shows a spatially increasing trend from the southwestern inland to the northeastern coast. Over the past 16 years, the salinization pattern has become more complex: soil salinization has been mitigated in the central and southern regions, with the average salinity decreasing from 1.03% to 0.86% and the salinization rate decreasing from 99% to 88%; and it has significantly intensified in the northern part of the study area near the old Yellow River channel and the Gubei Reservoir, and in some scattered inland areas. Continuous water and sediment regulation in the Yellow River basin and ecological management of natural reserves can generally alleviate soil salinization, but the risk of soil salinization can be increased by seawater erosion, inappropriate land use, and resource exploitation. [ABSTRACT FROM AUTHOR]

Spatial models are essential in the prediction of climate phenomena because they can model the complex relationships between different locations. In this study, we discuss an expanded spatial Durbin model with ordinary kriging on unobserved locations (ESDMOK) to predict rainfall patterns in Java Island. The classical spatial Durbin model needed to be expanded to obtain a parameter estimation for each location. We combined this with ordinary kriging because the data were not available in some locations. The data were taken from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) website. Since climate data are big data, we implement a big data analytics approach, namely the data analytics life cycle method. As the exogenous variables, we used air temperature, humidity, solar irradiation, wind speed, and surface pressure. The authors developed an R-Shiny web applications to implement our proposed technique. Using our proposed technique, we obtained more accurate and reliable climate data prediction, indicated by the mean absolute percentage error (MAPE), which was equal to 1.956%. The greatest effect on rainfall was given by the surface pressure variable, and the smallest was wind speed. [ABSTRACT FROM AUTHOR]

In this study, geostatistical techniques were used to investigate spatial variability of physico-chemical properties of soil samples from farming- and artisanal gold mining-affected areas. Based on coefficients of variation, chemical properties, except pH, had high variability compared to physical properties. This could be attributed to the local lithology, vegetation cover and land-use. Spatial distribution patterns of silt, clay, total nitrogen, pH and C/N could be controlled by extrinsic factors such as biological activity and land-use patterns, whereas those of electrical conductivity, organic matter (SOM) and sand were governed by both intrinsic and extrinsic factors. Because of the dominance of clay type, the distribution of cation exchange capacity (CEC) was spatially correlated with clay. About 80% of the area had acidic to circumneutral pH, which may increase mobility and toxicity of most cationic heavy metals. Distribution maps showed that microbial activities, expressed as C/N ratios, tend to be weak around farmlands, reflecting the impacts of soil management practices. In contrast, abundance of clay around the river bank is an indication of soil erosion and its redistribution from upland to depositional areas. Mean error and root mean square error of kriging were higher than those of cokriging for SOM, suggesting that cokriging was more reliable for spatial interpolation of this variable. The work reported in this study indicated that that 29%, 77% and 75% of the area had high probabilities to have pH, CEC and SOM contents below which most cationic heavy metals would be mobile and bioavailable in the soils.

Geostatistics tools like ordinary kriging was utilized to investigate the spatial variation in soil reaction (pH), electrical conductivity (EC), soil organic carbon (OC), calcium carbonate (CaCO3), available nitrogen (N), available phosphorus (P), available potassium (K), DTPA-extractable copper (Cu), zinc (Zn), manganese (Mn), and iron (Fe) in hot arid region of Western India. For this, total 132 surface soil samples (0–20 cm depth) were obtained with GPS coordinates from the study area. The study's finding showed that EC showed the highest variability (390%) whereas soil pH showed the least variability (3.72%). The best variogram fit model in ordinary kriging was selected on the basis of largest R2 values. For pH, EC CaCO3, N, K, Zn, and Fe, an exponential model imparted the best variogram fit, while a Gaussian model imparted the best variogram fit for OC, P, and Mn. Semi-variogram analysis (nugget/sill ratio) revealed that EC (0.25), CaCO3 (0.072) and N (0.027) were weakly spatial dependent, whereas pH (0.37), OC (0.456), P (0.598), K (0.70), Mn (0.57) and Fe (0.59) were moderately spatial dependent. However, Cu (1.0) and Zn (0.78) were strongly spatial dependent. The largest goodness-of-prediction criterion (G) values were found for the exponential model for soil pH, EC, CaCO3, N, K, Zn, and Fe while the largest G values were found for the Gaussian model for OC, P, and Mn. The exponential and Gaussian models of ordinary kriging, were able to map the spatial variations in investigated soil-variables. [ABSTRACT FROM AUTHOR]

Anthracnose is a fungal disease caused by Colletotrichum spp. that affects the avocado (Persea americana Mill.) crop and causes significant economic losses in the farming sector. To focus control measures, it is important to understand the spatial distribution and the dynamics followed by the disease under field conditions. The use of methods derived from spatial statistics facilitates this task. The aim of this study was to determine the spatial behavior of anthracnose in Persea americana Mill. cv. Hass in Donato Guerra, a municipality in the State of Mexico, Mexico, using a geostatistical and spatial analysis by distance indices. Four hundred trees were selected and georeferenced in the municipal area. In order to measure the incidence, 48 fruits were selected from every tree. Using these data, the experimental semivariogram was estimated, and adjustments were made to models that explain the spatial arrangement. Maps were created using ordinary kriging, and the infection area was estimated. The maps generated show the presence of aggregation centers and a spatial distribution mostly fitting Gaussian and exponential models, with ranges fluctuating between 12 and 56 m, indicating spatial association between data. Likewise, the greatest percentage of infected areas was 98 %, while the lowest was 45 %. Geostatistics enables a precise understanding of the distribution patterns of diseases such as anthracnose in avocado-growing areas of the State of Mexico, which facilitates the implementation of integrated management programs with greater effectiveness. [ABSTRACT FROM AUTHOR]

This investigation estimates a regional Mean Sea Surface (MSS) model, named SY21MSS, over the eastern Mediterranean Sea using satellite altimetry data from nine Exact Repeat Missions (ERM) and two Geodetic Missions (GM). Two interpolation methods, Least Squares Collocation (LSC) and Ordinary Kriging (OK), were employed, and statistical metrics were applied to assess their performance within a 15 km buffer from the coast. The comparison between LSC and OK techniques in the context of regional MSS modeling primarily centers on the covariance functions used by these methods. Furthermore, generalized cross-validation results indicate that OK outperforms LSC in this region. Consequently, the study recommends adopting the Kriging-based model for calculating regional MSS models in this region due to its superior performance. The investigation further explored the disparities between estimated regional MSS models and the global model DTU18MSS, highlighting a pronounced similarity between OK-SY21MSS and DTU18MSS, as evidenced by a lesser standard deviation (SD) difference compared to LSC-SY21MSS. The practical implications of this research underscore the importance of selecting an appropriate interpolation technique based on data characteristics and study area specifics. While both LSC and OK techniques are deemed viable for MSS modeling, the study emphasizes the superior performance of OK, particularly concerning covariance functions. Additionally, the results emphasize caution when applying global models in regions with significant local variations. [ABSTRACT FROM AUTHOR]

The article presents a study which assessed the spatial variations in soil textural fractions, bulk density and porosity of the lesser Himalayas in India. Topics include basic statistical values of the research area soil physical property data, reasons for the variation in soil characteristics, and computed semivariogram soil parameter characteristics.

Geostatistical prediction methods are increasingly used in earth sciences and engineering to improve upon our knowledge of attributes in space and time. During mining activities, it is very important to have an estimate of any contamination of the soil and groundwater in the area for environmental reasons and to guide the reclamation once mining operations are finished. In this paper, we present the geostatistical analysis of the water content in certain pollutants (Cd and Mn) in a group of mines in Northern Greece. The monitoring points that were studied are 62. The aim of this work is to create a contamination prediction map that better represents the values of Cd and Mn, which is challenging based on the small sample size. The correlation between Cd and Mn concentration in the groundwater is investigated during the preliminary analysis of the data. The logarithm of the data values was used, and after removing a linear trend, the variogram parameters were estimated. In order to create the necessary maps of contamination, we employed the method of ordinary Kriging (OK) and inversed the transformations using bias correction to adjust the results for the inverse transform. Cross-validation shows promising results ( ρ = 65 % for Cd and ρ = 52 % for Mn, RMSE = 25.9 ppb for Cd and RMSE = 25.1 ppm for Mn). As part of this work, the Spartan Variogram model was compared with the other models and was found to perform better for the data of Mn. [ABSTRACT FROM AUTHOR]

Modeling ecological patterns and processes often involve large‐scale and complex high‐dimensional spatial data. Due to the nonlinearity and multicollinearity of ecological data, traditional geostatistical methods have faced great challenges in model accuracy. As machine learning has increased our ability to construct models on big data, the main focus of the study is to propose the use of statistical models that hybridize machine learning and spatial interpolation methods to cope with increasingly large‐scale and complex ecological data. Here, two machine learning algorithms, boosted regression tree (BRT) and least absolute shrinkage and selection operator (LASSO), were combined with ordinary kriging (OK) to model plant invasions across the eastern United States. The accuracies of the hybrid models and conventional models were evaluated by 10‐fold cross‐validation. Based on an invasive plants dataset of 15 ecoregions across the eastern United States, the results showed that the hybrid algorithms were significantly better at predicting plant invasion when compared to commonly used algorithms in terms of RMSE and paired‐samples t‐test (with the p‐value <.0001). Besides, the additional aspect of the combined algorithms is to have the ability to select influential variables associated with the establishment of invasive cover, which cannot be achieved by conventional geostatistics. Higher accuracy in the prediction of large‐scale biological invasions improves our understanding of the ecological conditions that lead to the establishment and spread of plants into novel habitats across spatial scales. The results demonstrate the effectiveness and robustness of the hybrid BRTOK and LASOK that can be used to analyze large‐scale and high‐dimensional spatial datasets, and it has offered an optional source of models for spatial interpolation of ecology properties. It will also provide a better basis for management decisions in early‐detection modeling of invasive species. [ABSTRACT FROM AUTHOR]

This research paper has presented Unified Soil Classification System (USCS)–based soil type and Standard Penetration Test (SPT) N60–based zonation maps in the Dhaka Metropolitan Development Plan (DMDP) area of Bangladesh. For this purpose, 360 borehole data have been collected from different parts of the DMDP area. The data were analyzed in ArcGIS 10.5 using Ordinary Kriging and Inverse Distance Weighting (IDW) interpolation techniques. The spatial Analyst, Ordinary Kriging method, has demonstrated better results for SPT-N60-based, and IDW has provided superior outcomes of the USCS-based soil type zonation maps. This study has developed 13 SPT-N60-based and USCS soil-type zonation maps for the DMDP area at 1.5 to 19.5 m depth. To assess the reliability of the zonation map, SPT-N60 values and USCS soil classification, acquired through field tests at 10 locations (depths: 1.5 to 19.5 m), were compared with anticipated SPT-N60 values and USCS soil type at exact coordinates in the developed zonation maps. The root mean square error (RMSE) for the SPT-N60 zonation maps was 8.29 blows, and the coefficient of determination (R2) was 0.688. The comparison shows a correlation coefficient of 0.82. For USCS soil-type zonation maps, 55.81% of the field-validated data fully matched the model-anticipated USCS zonation maps data. The generated zonation maps in this research will help to understand the subsurface condition and geotechnical characteristics of soils in the DMDP area. This data will also help engineers to create an initial foundation layout for a new site using the existing data from the nearby locations. [ABSTRACT FROM AUTHOR]

The study area is situated in the Qelabshowah–Belqas region, known for its Quaternary deposits. This research aims to demonstrate the two-dimensional (2D) variation of subsurface layers and salinity distribution using geoelectrical data, hydrochemical analysis, and geostatistical analysis. DC resistivity measurements were taken at fifteen vertical electrical sounding (VES) survey points using a Schlumberger array (AB/2 = 100 m) along three profiles. In addition, an electrical resistivity tomography (ERT) survey was conducted with a dipole–dipole array across one profile. Seven surface water samples were collected in the area. From the 1D and 2D inversion of VES and ERT data sets, three-to-four geoelectric layers were identified, including unconsolidated surface deposits, saturated clayey sand, saturated sand, and a salt-rich layer. The 2D inversion of VES data revealed an ancient salt-rich layer deposited in swampy conditions over a conductive wet sand layer along profile one due to salt mineral infiltration and dissolution. The 2D inversion of ERT data showed accurate lateral geometric accuracy compared to the 2D inversion of VES data, highlighting geological features, such as caves in the second layer and a buried water canal on the ground surface. Surface water samples showed high salinity levels with sodium hazards, indicating an Na–Cl composition. Geoelectric and hydrochemical data sets were geostatistically analyzed using spherical variogram supported ordinary Kriging interpolation. The analysis indicated weak to moderate spatial dependency for true resistivity parameters, while sodium content (SC) and permeability index (PI) showed strong spatial correlation. The 2D spatial distribution resistivity maps based on the 1D inversion of VES data displayed a general decrease in resistivity with depth, likely due to clay minerals or moist soil in the second layer and saline irrigation water infiltration in the third layer. The 2D spatial distribution of SC and PI showed a high concentration zone, posing a potential risk to agricultural crops regardless of soil permeability. It is recommended to use these maps when cultivating plants that can tolerate high sodium levels during the reclamation process. [ABSTRACT FROM AUTHOR]

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This paper dealt with the study of the analysis and interpretation of spatial variability using the Kriging technique in geostatistics. The objectives of this work are; to interpolate the values of regionalized variables; to express the spatial variation after using the logarithmic transformed for the original scale; observations for two groups of soil data; To reduce the level of pollution in the soil by studying the characteristics of the estimates. The ordinary kriging procedure is used to estimate the best linear unbiased estimator. The experimental semi-variogram function is applied as a tool to give the idea of spatial distribution after using the logarithmic transformations of the origin data. This method assumes the isotropy. Also, a robust estimate (Matheron's and Haslett's, Cressie-Hawkins) was applied to minimize some prediction scores. Data adopted in this work is taken from Mosul city in Iraq, for some soil spatial real data. Each data contains (100) real soil data of (PH) and (NO3). Our finding results illustrate the variance is itself for all directions of the compass: East-west, North-south, Northeast, and Northwest. The model describes (94%) nearest the Gaussian model of (PH), and (92%) nearest the spherical model of the total variability of (NO3) after comparing the results models between the original scale and the lognormal data by obtain the fitting model of soil data with the formulas of kriging. In conclusion, we show the qualities of the estimation rely on the ratio distances. Behaviors of continues of the phenomenon or observations and low coefficient of variation, which leads to improved efficiency in spatial distribution The support of results show that Matheron's and Haslett's robust estimators had better performance than Cressie Hawkins's robust soil data comparison with the curves of variogram function, because the small effect of outlier values on the estimates it is clear from this effect that pollution may be large by correctly knowing the weight restrictions for the level of pollution, and reducing the level of pollution depends practically and for a long period on the stationarity of some estimates. All computations are carried out in Matlab language. [ABSTRACT FROM AUTHOR]

The growth of a city is typically accompanied by densification and sprawl, the former through verticalization, urban renewal, and the filling in of empty spaces. All of these activities extend and intensify the urban heat island (UHI), which is quantified in this study as the difference in daily minimum temperature between urban and rural areas. Here, we investigate this phenomenon in the area of Rennes (France) and 17 surrounding cities using the Rennes Urban Network which comprises 93 weather stations. This study aims to 1) determine the optimal method for spatializing UHI in Rennes, France, 2) estimate and spatialize the UHI in the small peri-urban cities surrounding Rennes. For this, we model mean UHI and intense UHI using three methods of interpolation—multi-linear regression (MLR), ordinary kriging (OK), and regression kriging (RK)—based on data from 2022. We find that the RK method is the most suitable overall, with an RMSE of 0.11°C for mean UHI and 0.25°C for intense UHI. This approach allows stochasticity to be taken into account, and thus provides a better representation of UHI variation within Rennes and its peri-urban cities.

One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to accurately reveal the spatial heterogeneity of P. Seven spatially variable fields located in Ontario, Canada are clustered into two zones; four fields are located in eastern Ontario and three others are located in western Ontario. This study compares Bayesian Additive Regression Trees (BART), Support Vector Machine regressor (SVM), and Ordinary Kriging (OK), along with novel data fusion concepts, to analyze integrated high-density spatial data layers related to spatial variability in soil available P. Feature selection and interaction detection using BART variable selection and Recursive Feature Elimination (RFE) for SVM were applied to 42 predictors, including soil-vegetation indices derived from PlanetScope multispectral imagery, high-density apparent soil electrical conductivity (ECa), and high-resolution topographic attributes derived from DUALEM-21S and a Real-Time Kinematic (RTK) global navigation satellite systems (GNSS) receiver, respectively. Modeling spatial heterogeneity of soil available P with BART showed higher accuracy than SVM and OK in both zones of this study when trained and tested on ground truth data from clusters of farms. A BART variable selection approach resulted in six auxiliary predictors of soil available P in the eastern zone, while only four predictors were selected to predict P in the western zone. RFE for SVM resulted in models with 15 and 12 auxiliary predictors in the eastern and western Ontario zones. Topographic elevation was the most influential predictor of soil available P in both zones. Compared with the SVM and OK methods, BART exhibited lower average RMSE values for individual fields of 1.86 ppm and 3.58 ppm across the eastern and western Ontario zones, respectively, along with higher R2 values of 0.85 and 0.83, respectively. In contrast, SVM had RMSE values for individual fields in the eastern and western Ontario zones, respectively, averaging 5.04 ppm and 7.51 ppm and R2 values of 0.27 and 0.43. RMSE values for soil available P in individual fields across the eastern and western Ontario zones averaged 4.77 ppm and 7.81 ppm, respectively, with the OK method, while R2 values averaged 0.19 and 0.44. The selection of suitable auxiliary predictors and data fusion, combined with BART spatial machine learning algorithms, have potential to be a useful tool to accurately estimate spatial patterns in soil available P for agricultural fields in Ontario, Canada. [ABSTRACT FROM AUTHOR]

Fine-resolution spatio-temporal maps of near-surface urban air temperature (Ta) provide crucial data inputs for sustainable urban decision-making, personal heat exposure, and climate-relevant epidemiological studies. The recent availability of IoT weather station data allows for high-resolution urban Ta mapping using approaches such as interpolation techniques or machine learning (ML). This study is aimed at executing these approaches and traditional numerical modeling within a practical and operational framework and evaluate their practicality and efficiency in cases where data availability, computational constraints, or specialized expertise pose challenges. We employ Netatmo crowd-sourced weather station data and three geospatial mapping approaches: (1) Ordinary Kriging, (2) statistical ML model (using predictors primarily derived from Earth Observation Data), and (3) weather research and forecasting model (WRF) to predict/map daily Ta at nearly 1-km spatial resolution in Warsaw (Poland) for June–September and compare the predictions against observations from 5 meteorological reference stations. The results reveal that ML can serve as a viable alternative approach to traditional kriging and numerical simulation, characterized by reduced complexity and higher computational speeds within the domain of urban meteorological studies (overall RMSE = 1.06 °C and R2 = 0.94, compared to ground-based meteorological stations). The results have implications for identifying the urban regions vulnerable to overheating and evidence-based urban management in response to climate change. Due to the open-sourced nature of the applied predictors and input parsimony, the ML method can be easily replicated for other EU cities. [ABSTRACT FROM AUTHOR]

The health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including variography, ordinary kriging, and G clustering, was used to generate national distribution maps for various factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers. [ABSTRACT FROM AUTHOR]

In the Algerian-Tunisian border region lies the Tebessa plain, which belongs to the Saharan Atlas domain and is characterized by a semi-arid climate and high salinity, often rendering the groundwater unusable. The objective of this research is to highlight groundwater salinization using statistical and geostatistical methods to model this spatial heterogeneity. A total of 30 data points were used, and the statistical analysis revealed a strong association between chlorides, sulfates, electrical conductivity, and values significantly exceeding the standards. Surface and directional variograms for EC (electrical conductivity), SO4 (sulfates), and Cl (chlorides) exhibit anisotropy, with the major axis showing a hole effect and a periodicity of 5 km. Geostatistical mapping through ordinary kriging allowed us to identify areas of high salinity and deduce the underlying cause; which is the dissolution of gypsiferous formations. These results were confirmed by thermodynamics and Sr2+/Ca2+ ratio. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The accurate prediction of soil contamination in abandoned mining areas is necessary to address their environmental risks. This study employed a combined model of machine learning and geostatistics to predict the spatial distribution of soil contamination using heavy metal data collected in an abandoned metal mine. An exploratory data analysis was used to identify patterns in the collected data, the root mean squared error (RMSE) and coefficient of determination (R2) were used to verify the predicted values, and the model was validated using K-fold cross-validation. The prediction results were produced as a map by applying hyperparameter tuning to Random Forest (RF) and Ordinary Kriging (OK) through GridSearchCV using optimal parameter selections. Furthermore, the prediction residuals of the RF model were calculated, and the RF prediction map and OK interpolation results of the RF prediction residuals were summarized to construct an RF–OK prediction map. The RMSE and R2 values for the RF, OK, and RF–OK interpolation models were 66.214, 65.101, and 52.884 mg/kg and 0.867, 0.871, and 0.915, respectively. In addition, the optimization results with the minimum RMSE and maximum R2 were obtained through hyperparameter tuning. The proposed RF–OK hybrid model demonstrated superior prediction performance compared to the individual models. [ABSTRACT FROM AUTHOR]

Ordinary kriging (OK) is a popular interpolation method for its ability to simultaneously minimize error variance and deliver statistically optimal and unbiased predictions. In this work, the adaptive moving window kriging with K-means clustering (AMWKK) technique is developed to improve the estimation obtained from the moving window kriging based on the K-means clustering proposed by Abedini et al. This technique specifically addresses the challenge of selecting appropriate windows for target points located near the borders, which can potentially be the source of errors. The AMWKK algorithm introduces a dynamic clustering approach within the moving window kriging, where each target site sequentially serves as a cluster centroid. The OK is then applied within the cluster encompassing the target point, ensuring localized and adaptive interpolation. The proposed method is compared with ordinary kriging and other moving window kriging variant approaches to estimate Thailand's mean annual pressure and humidity in 2018. The results indicate superior estimation capabilities of the AMWKK approach in terms of distinct quantitative performance statistics. The advantage of using the AMWKK method for spatial interpolation can be attributed to the fact that it facilitates the automatic tuning of the window size at any estimation point. The algorithm is particularly effective when observations in the same cluster as target points are sparse. [ABSTRACT FROM AUTHOR]

Abstract Modeling ecological patterns and processes often involve large‐scale and complex high‐dimensional spatial data. Due to the nonlinearity and multicollinearity of ecological data, traditional geostatistical methods have faced great challenges in model accuracy. As machine learning has increased our ability to construct models on big data, the main focus of the study is to propose the use of statistical models that hybridize machine learning and spatial interpolation methods to cope with increasingly large‐scale and complex ecological data. Here, two machine learning algorithms, boosted regression tree (BRT) and least absolute shrinkage and selection operator (LASSO), were combined with ordinary kriging (OK) to model plant invasions across the eastern United States. The accuracies of the hybrid models and conventional models were evaluated by 10‐fold cross‐validation. Based on an invasive plants dataset of 15 ecoregions across the eastern United States, the results showed that the hybrid algorithms were significantly better at predicting plant invasion when compared to commonly used algorithms in terms of RMSE and paired‐samples t‐test (with the p‐value

Spatial models are essential in the prediction of climate phenomena because they can model the complex relationships between different locations. In this study, we discuss an expanded spatial Durbin model with ordinary kriging on unobserved locations (ESDMOK) to predict rainfall patterns in Java Island. The classical spatial Durbin model needed to be expanded to obtain a parameter estimation for each location. We combined this with ordinary kriging because the data were not available in some locations. The data were taken from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) website. Since climate data are big data, we implement a big data analytics approach, namely the data analytics life cycle method. As the exogenous variables, we used air temperature, humidity, solar irradiation, wind speed, and surface pressure. The authors developed an R-Shiny web applications to implement our proposed technique. Using our proposed technique, we obtained more accurate and reliable climate data prediction, indicated by the mean absolute percentage error (MAPE), which was equal to 1.956%. The greatest effect on rainfall was given by the surface pressure variable, and the smallest was wind speed.

In decommissioning projects of nuclear facilities, radiological characterisation aims to estimate the quantity and spatial distribution of different radionuclides. To carry out the estimation, measurements are performed on site to obtain preliminary information. The usual industrial practice consists of applying spatial interpolation tools (as the ordinary kriging method) on these data to predict the value of interest for the contamination (radionuclide concentration, radioactivity, etc.) at unobserved positions. This paper questions the ordinary kriging tool on the well-known problem of the overoptimistic prediction variances due to not taking into account uncertainties on the estimation of the kriging parameters (variance and range). To overcome this issue, the practical use of the Bayesian kriging method, where the model parameters are considered as random variables, is deepened. The usefulness of Bayesian kriging, whilst comparing its performance to that of ordinary kriging, is demonstrated in the small data context (which is often the case in decommissioning projects). This result is obtained via several numerical tests on different toy models and using complementary validation criteria: the predictivity coefficient ( Q 2 ), the predictive variance adequacy, the α confidence interval plot (and its associated mean squared error α ), and the predictive interval adequacy. The latter is a new criterion adapted to Bayesian kriging results. Finally, the same comparison is performed on a real data set coming from the decommissioning project of the CEA Marcoule G3 reactor. It illustrates the practical interest of Bayesian kriging in industrial radiological characterisation. [ABSTRACT FROM AUTHOR]

Copyright of Sigmae is the property of Universidade Federal de Alfenas (UNIFAL-MG) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

THE MAJOR aim of the study was to assess and map the spatial variability of some soil properties in El-Gallaba Plain, New Aswan City, using a geostatistical technique. Forty topsoil samples were selected from forty profiles that were dug to represent the study area. The variability of the soil maps was drawn based on the ordinary kriging interpolation method based on the geostatistical analysis. The data indicates that most of the soil samples were rough in texture. The organic matter was extremely low in most soil samples (≤ 4.03 g kg-1). The salinity of soil paste extract (ECe) ranges from 0.84 to 28.21 dSm-1. The soil reaction (pH) values of the surface soils vary between 7.69 and 8.89. The calcium carbonate values extend between 0.43 and 9.74 %. Gypsum contents in the soil samples range between 0.49 to 4.07%. The CEC of soil samples ranged between 3.73 and 25.35 cmolc (+)/kg. The coefficient of variation of soil pH was low (CV<5%), medium for sand fraction (CV<25%), and the rest of the soil properties were high to very high in the coefficient of variation. The normal histogram and QQPlots analysis of the physicochemical properties of the studied soil samples was applied to make the data more normally distributed. Logarithmic transformation of the soil properties data was used to normalize highly skewed and distant datasets because ordinary kriging methods work best if the data are approximately normally distributed. The ordinary Kriging (OK) method was used in the present study as an interpolation method compared to other Kriging methods due to it being simple and having high accuracy for prediction. The data reveal that the Gaussian, J-Bessel, Exponential, Rational Quadratic, and K-Bessel are the best-fitted semivariogram models for all properties selected. Accurate maps efficiently generated using geostatistics were essential to properly understand the spatial variability of the area under study. This study gives useful information about the physical and chemical characteristics and the spatial diversity of this soil. [ABSTRACT FROM AUTHOR]

Recently, new numerical methods have been applied to weather data for the estimation of water budget, especially when the lack of measured data is considerable. Geostatistics is one of the most powerful approaches when it comes to studying spatially relevant natural phenomena, as it considers the spatial correlation among measurements over a specific study area and provides the associate uncertainty. In this study, we tested the feasibility of using a geostatistical method to provide a reliable estimation of the water budget of the Foro river basin (Central Italy) by comparing the obtained results with those of a traditional yet robust method. The results obtained with the geostatistical approach proved to be in line with the ones from the traditional method. Additionally, it was possible to quantify the uncertainty associated with the discharge values, making the estimates more reliable than the ones obtained with the traditional approach. However, the yearly distribution of river discharge obtained using both methods appeared to be dissimilar to the measured ones. The surface water uses, as well as the regulatory effect of the carbonate and alluvial aquifer regime, may affect the river discharge variability over the year and then can account for similar discrepancies between the inflow and outflow water volumes. [ABSTRACT FROM AUTHOR]

An increase in population expansion, urban sprawling environment, and climate change has resulted in increased food demand, water scarcity, environmental pollution, and mismanagement of water resources. Groundwater, i.e., one of the most precious and mined natural resources is used to address a variety of environmental demands. Among all, irrigation is one of the leading consumers of groundwater. Various natural heterogeneities and anthropogenic activities have impacted the groundwater quality. As a result, monitoring groundwater quality and determining its suitability are critical for the sustainable long-term management of groundwater resources. In this study, groundwater samples from 35 different sampling stations were collected and tested for various parameters associated with irrigation water quality. Hybrid MCDM (fuzzy-AHP) method was used to determine the groundwater suitability for irrigation purposes. The suitability map obtained using spatial overlay analysis was classified into low, moderate, and high irrigation water suitability zones. Along with suitability analysis, various regression-based machine learning models such as multiple linear regression (MLR), random forest (RF), and artificial neural network (ANN) were used and compared to predict irrigation water suitability. Results depicted that the ANN model with the highest R2 value of 0.990 and RMSE value near to zero (0) has outperformed all other models. The present methodology could be found useful to predict irrigation water suitability in the region where regular sampling and analysis are quite challenging. [ABSTRACT FROM AUTHOR]

Precipitation is a key constituent of the water cycle and its accurate measurement is essential for a wide range of hydroclimatic studies. Although advances in technology have provided new sources of precipitation data (e.g., satellite data), gauge measurements are still considered as the most reliable source of information. Accuracy of ground measurements highly depends on the network density and positions of the rain gauges and thus, many studies have attempted to re-design the rain gauge networks around the globe. This is while, a limited number of researches have investigated the potential of utilizing global gridded precipitation products in network optimization context. In this study, a two-step framework was proposed to remove redundant rain gauge stations of the Central Plateau watershed of Iran. In the first step, number of excess rain gauges was detected based on transinformation–distance (T–D) analysis. In the second step, exact location of the extra rain gauges was determined through high degree of freedom and low degree of freedom (LDF) scenarios by a combination of harmony search optimization algorithm and a geostatistical model. According to the results, 58 out of 580 rain gauges were detected to be removed. Results of the proposed scenarios differed in detecting 13 out of 58 gauges. While LDF scenario required less computational time, network re-designed by this scenario was about 32% less accurate than the other scenario. Performance of the both optimized networks was equally similar to the initial network in terms of developing precipitation rasters. [ABSTRACT FROM AUTHOR]

The radon-prone area of the Adamawa region in Cameroon is characterized by high natural radiation background resulting from the high concentrations of radium-226, thorium-232, and indoor radon. To produce a radon-risk map, radon measurements in soil were carried out in the city of Ngaoundere. The radon activity concentration in soil gas ranged from 256 to 166 kBq m−3 with a mean of 80 kBq m−3 and a standard deviation of 38 kBq m−3. The area is mostly classified as high risk (80%) according to the Swedish classification, and 20% as medium risk. A low-risk area was not observed. Granite-like geology sites were characterized by higher radon concentration. A ratio of about 295:1 was obtained for soil radon gas to indoor radon concentrations, with a positive correlation (R = 0.40), and a transfer factor of 3 per mil. These results demonstrate that in situ measurements of radon concentration in soil can provide accurate information on the level of indoor radon concentrations. Geostatistical and deterministic interpolation techniques have been used to obtain a radon map by comparing the suitability of ordinary kriging and inverse-distance-weighted (IDW) interpolation methods. It turned out that there is not much difference in the prediction errors of the two techniques (Root Mean Square Error = 34.4 for ordinary kriging and 34.3 for IDW). It is concluded that both methods give acceptable results. In situ measurements and geostatistical analysis allow assessment of expected indoor radon exposure in a given area at reduced costs and time required. However, for the investigated area, more research is needed to produce reliable radon-risk maps. [ABSTRACT FROM AUTHOR]