Geostatistical Approach Research Articles

Geostatistical Approach to Determining the Pressure Head Spatial Distribution along the Center-Pivot Lateral Line

Geostatistical Approach to Determining the Pressure Head Spatial Distribution along the Center-Pivot Lateral Line

Bias evaluation and minimization for estuarine total dissolved solids (TDS) patterns constructed using spatial interpolation techniques

This study evaluated and minimized bias in estuarine total dissolved solids (TDS) patterns predicted using geostatistical approaches. The acquired TDS data was divided into three parts: 29 points (60 %) for predicting TDS patterns with spatial interpolation techniques, 12 points (25 %) for validation and bias correction, and 7 points (37 %) for testing the corrected bias. Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Universal Kriging (UK) and Regression Kriging (RK) were applied to map TDS patterns. R-square and Relative Error of Mean showed significant discrepancies between observed and predicted TDS levels. The Mean field bias (MFB) correction technique was applied to minimize bias in TDS patterns. After correcting bias, the TDS values predicted by IDW, OK, UK and RK at random locations deviated from measured values by −2.85 %, 0.71 %, 4.66 %, and − 6.03 % respectively. At the test locations, these values deviated by −1.45 %, 1.41 %, 2.11 %, and − 2.65 % respectively.

Quantification and validation of uncertainties in subsoil models

AbstractIn infrastructure planning and construction, modeling the subsoil and its associated uncertainty is a fundamental task of geotechnical engineers. However, probabilistic methods and tools for quantifying and displaying the uncertainty of the subsoil models are rarely used in practice where deterministic interpolation dominates. In digital planning using Building Information Modeling (BIM), the probabilistic approach supports creating a discipline model in which the uncertainties of the spatial layer structure are statistically quantified to evaluate the georisks in the design and execution of civil constructions. This article presents a case study using a combination of Sequential Gaussian Simulation (SGSIM) and Sequential Indicator Simulation (SISIM) to account for uncertainties in soil layer geometry. In a case study at the Munich Town Hall, a geostatistical approach is applied and validated based on 70 bore logs, whereby the probabilities for the occurrence of a particular layer are spatially quantified. The case study illustrates the methodology‘s great potential and benefits compared to the conventional deterministic approach based on interpolation procedures.

Terrain Analysis According to Multiscale Surface Roughness in the Taklimakan Desert

Surface roughness, interpreted in the wide sense of surface texture, is a generic term referring to a variety of aspects and scales of spatial variability of surfaces. The analysis of solid earth surface roughness is useful for understanding, characterizing, and monitoring geomorphic factors at multiple spatiotemporal scales. The different geomorphic features characterizing a landscape exhibit specific characteristics and scales of surface texture. The capability to selectively analyze specific roughness metrics at multiple spatial scales represents a key tool in geomorphometric analysis. This research presents a simplified geostatistical approach for the multiscale analysis of surface roughness, or of image texture in the case of images, that is highly informative and interpretable. The implemented approach is able to describe two main aspects of short-range surface roughness: omnidirectional roughness and roughness anisotropy. Adopting simple upscaling approaches, it is possible to perform a multiscale analysis of roughness. An overview of the information extraction potential of the approach is shown for the analysis of a portion of the Taklimakan desert (China) using a 30 m resolution DEM derived from the Copernicus Glo-30 DSM. The multiscale roughness indexes are used as input features for unsupervised and supervised learning tasks. The approach can be refined both from the perspective of the multiscale analysis as well as in relation to the surface roughness indexes considered. However, even in its present, simplified form, it can find direct applications in relation to multiple contexts and research topics.

Hydrological Modeling to Unravel the Spatiotemporal Heterogeneity and Attribution of Baseflow in the Yangtze River Source Area, China

Revealing the spatiotemporal variation in baseflow and its underlying mechanisms is critical for preserving the health and ecological functions of alpine rivers, but this has rarely been conducted in the source region of the Yangtze River (SRYR). Our study employed the Soil and Water Assessment Tool (SWAT) model coupled with two-parameter digital filtering and geostatistical approaches to obtain a visual representation of the spatiotemporal heterogeneity characteristics of the baseflow and baseflow index (BFI) in the SRYR. The SWAT model and multiple linear regression model (MLR) were used to quantitatively estimate the contribution of climate change and human activities to baseflow and BFI changes. The results underscore the robust applicability of the SWAT model within the SRYR. Temporally, the precipitation, temperature, and baseflow exhibited significant upward trends, and the baseflow and BFI showed contrasting intra-annual distribution patterns, which were unimodal and bimodal distribution, respectively. Spatially, the baseflow increased from northwest to southeast, and from the watershed perspective, the Tongtian River exhibited higher baseflow values compared to other regions of the SRYR. The baseflow and BFI values of the Dangqu River were greater than those of other tributaries. More than 50% of the entire basin had an annual BFI value greater than 0.7, which indicates that baseflow was the major contributor to runoff generation. Moreover, the contributions of climate change and human activities to baseflow variability were 122% and −22%, and to BFI variability, 60% and 40%. Specifically, precipitation contributed 116% and 60% to the baseflow and BFI variations, while the temperature exhibited contributions of 6% and 8%, respectively. Overall, it was concluded that the spatiotemporal distributions of baseflow and the BFI are controlled by various factors, and climate change is the main factor of baseflow variation. Our study offers valuable insights for the management and quantitative assessment of groundwater resources within the SRYR amidst climate change.

Geostatistical Simulation of Daily Rainfall Fields—Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Nutrient Potential Mapping of Soils for Tea Plants Through Laboratory and Geostatistical Approaches

<p>The assessment of nutrient potential is crucial for enhancing tea production and achieving sustainable agricultural goals. Thus, the present study was carried out to assess and map the soil nutrient potential for tea plants through laboratory and geostatistical methods. A total of 74 random soil samples with 3 replications were collected from 0 to 9 cm (topsoil) and 9 to 18 cm (subsoil) depths from Oodaleah Tea Garden (OTG) which belongs to Fatikchhari Upazila of Chattogram District in Bangladesh. All the laboratory analyses were done following standard procedures and maps of individual soil indices were prepared through the Inverse Distance Weighted (IDW) interpolation technique using ArcGIS 10.7 software. The integration of maps for nutrient potential was performed following the Analytic Hierarchy Process (AHP). The mean concentrations of organic matter (OM), total nitrogen (TN), available phosphorus (AvP), available potassium (AvK), and available calcium (AvCa) were found higher at 0 to 9 cm compared to 9 to 18 cm soil depth with significant (<em>p</em> < 0.05) difference among the sampling sites. The maps of individual soil parameters revealed distinct spatial differences at 0 to 9 cm and 9 to 18 cm soil depths. The total study area covered 5.99 km<sup>2</sup>. Integrated nutrient potential mapping showed that moderately suitable (44.15%) accounted for the largest proportion, followed by highly suitable (26.03%), generally suitable (21.67%), marginally suitable (7.06%), and unsuitable area (1.08%) of the tea plantation suitability evaluation. The integrated map of nutrient potential will aid in better management of the tea soils.</p>

PNN Enhanced Seismic Inversion for Porosity Modeling and Delineating the Potential Heterogeneous Gas Sands via Comparative Inversion Analysis in the Lower Indus Basin

Seismic inversion has been in use for the last two decades to measure inverted impedances using an integrated data set approach. This research focuses on the application of multi-attribute seismic inversion and the geostatistical probabilistic neural network (PNN) approach for determining rock properties and litho-fluid classification in the Mehar-Mazarani Field of the Lower Indus Basin (LIB), Pakistan. The study compares five different inversion techniques, including model-based inversion (MBI), colored inversion (CI), linear sparse spike inversion (LSSI), band-limited inversion (BLI), and maximum likelihood sparse spike inversion (MLSSI). The inverted outputs, such as acoustic P-impedance (Zp), density (ρ), porosity (φ), and shale volume (Vsh), were analyzed in Paleocene and Cretaceous geological complex reservoirs to identify gas-bearing zones. The results indicated the existence of gas between 1630 and 1700 ms (ms) and corresponding depth ranges from approximately 3200 m up to 4200 m with varying thickness. Amongst the inversion techniques, MBI demonstrated greater accuracy, with inverted density volumes showing a strong correlation coefficient of 0.98 and the lowest root mean square error (RMSE) and relative error of 0.10 m/s * g/cc. A geostatistical PNN approach was employed to estimate variations in Vsh and φ within the sand reservoir. MBI again yielded more reliable results, with a strong correlation between the measured and inverted attributes. High φ and low Vsh were observed in predetermined low-impedance zones. Overall, MBI is proven to be the most accurate and reliable technique, providing clear identification of the gas occurrence. This research highlights the effectiveness of seismic inversion, particularly the application of MBI, in determining rock properties and identifying gas-bearing zones within the Mehar-Mazarani gas field.

Physics-informed geostatistical approach (PIGA) for real-time fluvial flood modeling

This study presents the Physics-Informed Geostatistical Approach (PIGA), a novel methodology aimed at improving flood prediction accuracy in riverine systems during hurricane events. PIGA integrates reduced spatial correlations derived from fluvial hydrodynamic models with a reduced geostatistical approach (RGA) based on monitoring data to provide real-time estimations of water surface elevations (WSE) across river channels and reaches. Focusing on the Savannah River basin, an area prone to riverine flooding during hurricane seasons, we developed a 1D-2D HEC-RAS model as the physical model and compared its performance with PIGA for various hurricane events, including Hurricane Matthew, Irma, Dorian, and Idalia. Our findings reveal that PIGA consistently outperforms the HEC-RAS model in predicting WSE, particularly in areas with limited bathymetry data. Statistical analyses demonstrate significant improvements in prediction accuracy achieved by PIGA, emphasizing its potential as a reliable tool for flood prediction in data-deficient regions. Additionally, we explore the transferability of reduced spatial correlations across different hurricane events. Our results indicate that spatial correlations derived from one hurricane event can effectively predict WSE for other events, with Hurricane Irma’s spatial correlations proving to be the most versatile for our study site. However, we also observe variability in spatial correlations among different hurricane events, highlighting the importance of selecting the optimal alternative spatial reference for prediction. Furthermore, we highlight PIGA’s computational efficiency, with simulations completed in significantly less time compared to traditional physical models, rendering its potential to provide real-time prediction of WSE during flooding events.

Pan India fluoride hazard assessment in groundwater

Fluoride (F¯) contamination in groundwater in India has gained global attention due to human health hazards. India’s hydrogeological heterogeneity, spatio-temporal variability of F¯, and health hazards due to geogenic and geo-environmental control pose unique challenges. Addressing these with only a single region-specific study is not possible. Therefore, this study provides an in-depth, holistic analysis of pan India F¯ contamination, controlling factors, and health hazards using a coupled advanced geostatistical and geospatial approach. Alarming F¯ contaminations are identified in Rajasthan, Telangana, Western Andhra Pradesh, Eastern Karnataka, Parts of Haryana, Gujarat, Madhya Pradesh, Tamil Nadu, Uttar Pradesh, Jharkhand, Bihar, and Chhattisgarh. Probabilistic health-risk evaluation using hot-spot, showed similar spatio-temporal distribution of F¯ contamination. The hazard quotient (HQ) for high F¯ shows more adversity to children than adults. Nationally, 8.65 % and 7.10 % of pre- and post-monsoon sites exceed the recommended safe limit of 1.50 mg/L. The highest average F¯ concentration is in Rajasthan. Very high-risk skeletal fluorosis is possible at around ≤ 2 %, whereas dental caries due to deficiency in F¯ concentration is approximately 40 %. A decisive hierarchy of lithology, geomorphology, soils, and lineaments control are identified on F¯ contamination. Climatic conditions are pivotal in governing all these controlling variables. Thus, in arid/semi-arid dry western regions, F¯ contamination is much higher than in the humid areas. Integration of strengths, weaknesses, opportunities, and threats (SWOT) analysis with the results can aid policymakers and government authorities in achieving sustainable remedial measures for future adaptability.

Geochemical anomaly separation based on geology, geostatistics, compositional data and local singularity analyses: A case study from the kuh panj copper deposit, Iran

This study combines geochemical anomaly separation with geostatistical approaches and compositional data analysis. To have a reasonable model for abnormal areas, suggesting the optimal drilling coordinates, one needs to consider some effective geological controllers on the mineralization such as lithology, alteration, and tectonic processes. The lithogeochemical samples at 608 locations were processed from the copper porphyry mineralization area of Kuh Panj, Iran, to illustrate the proposed approach. The heterogeneity of rock type domains was detected using contact analysis. Based on this, plurigaussian simulation made it possible to model the uneconomic dykes of the area. Copper-molybdenum grades and filler as geochemical components were transformed into multivariate normal data using isometric logarithmic ratio-flow anamorphosis. Hierarchical rock type-grade simulation reproduced the grade behavior at hard rock type boundaries. Selection of a dense and regular simulation grid minimized the smoothing effect caused by interpolation of the singularity index values. Finally, unsupervised machine learning identified the anomaly zone by clustering the results of the singularity analysis. Validation using drilling data and the geological map shows that the anomaly separation, considering the geological conditions and the compositional nature of the geochemical data, can provide a reliable model of the geochemical anomalies in the Kuh Panj deposit.

Mapping of on-field soil nutrient variabilities as a guiding force for smart farming: a case study from FarmerZone sentinel-1 from three potato agroecological zones of India.

Mapping of soil nutrient parameters using experimental measurements and geostatistical approaches to assist site-specific fertiliser advisories is anticipated to play a significant role in Smart Agriculture. FarmerZone is a cloud service envisioned by the Department of Biotechnology, Government of India, to provide advisories to assist smallholder farmers in India in enhancing their overall farm production. As a part of the project, we evaluated the soil spatial variability of three potato agroecological zones in India and provided soil health cards along with field-specific fertiliser recommendations for potato cultivation to farmers. Specifically, 705 surface samples were collected from three representative potato-growing districts of Indian states (Meerut, UP; Jalandhar, Punjab and Lahaul and Spiti, HP) and analysed for soil parameters such as organic carbon, macronutrients (NPK), micronutrients (Zn, Fe, Mn, and Cu), pH, and EC. The soil parameters were integrated into a geodatabase and subjected to kriging interpolation to create spatial soil maps of the targeted potato agroecological zones through best-fit experimental semivariograms. The spatial distribution showed a deficiency of soil organic carbon in two studied zones and available nitrogen among all studied zones. The available phosphorus and potassium varied among the agroecological zones. The micronutrient levels were largely sufficient in all the zones except at a few specific sites where nutrient advisories are recommended to replenish. The general management strategies were recommended based on the nutrient status in the studied area. This study clearly supports the significance of site-specific soil analytics and interpolated spatial soil mapping over any targeted agroecological zones as a promising strategy to deliver reliable advisories of fertiliser recommendations for smart farming.

Spatial distribution of soil rare earth elements in Sicily (Italy)

Rare earth elements (REEs) are becoming increasingly interesting as indicators of soil processes, and modelling their spatial distribution has become a fundamental requirement for this purpose. This study aims to model and quantify the spatial distribution of soil REEs taking into account their compositional nature in their mapping, and to assess the associated spatial uncertainty. In particular, the cerium anomaly (Ce⁎) and the ratio of the sum (Σ) of light to heavy REEs (ΣLREEs/ΣHREEs) were calculated and analysed. The study was carried out in Sicily (Italy), one of the largest (25,832 km2) and most populous island in the Mediterranean Sea. Soil REE data were obtained from the soil database of Sicily region. Soil REEs were transformed into a vector of isometric log-ratio (ilr) coordinates and analysed using a geostatistical approach for predicting their values at unsampled locations and generating 100 REEs realizations using turning bands simulation. Each REE element and Ce⁎ were mapped as well as the ΣLREEs/ΣHREEs ratio. The joint variability of the investigated REEs and the ΣLREEs/ΣHREEs ratio provided insights into their abundance and distribution patterns. From the simulated realizations, standard deviation maps were calculated for the Ce⁎ and the ΣLREEs/ΣHREEs ratio, providing a measure of their spatial uncertainty. Although with some limitations, the study established a first baseline of the Ce⁎ and the ΣLREEs/ΣHREEs ratio. The maps of spatial uncertainty may be a useful tool for planning future soil sampling and optimise the choice of new sampling locations.

Results of the CO2 diffuse degassing survey from the 2017 IAVCEI CCVG 13th volcanic gas workshop: Pululahua Dome Complex, Ecuador

Pululahua is a potentially active andesite and dacite lava dome complex. This paper presents the results of a survey focused on carbon dioxide (CO2) diffuse degassing at Pululahua, which was conducted during the 2017 International Association of Volcanology and Chemistry of the Earth's Interior (IAVCEI) Commission of the Chemistry of Volcanic Gases (CCVG) 13th Gas Workshop. Our objective was to conduct a comprehensive investigation of CO2 diffuse degassing by employing standard methods for measuring CO2 flux and temperature, and data processing. These methods were applied to map the spatial distribution of the measured parameters, investigate the origin of CO2, and quantify the volcanic CO2 output within the surveyed area of Pululahua. We carried out a total of 350 soil CO2 flux and 329 soil temperature measurements and collected 12 gas samples for carbon isotopic composition analysis, surrounding the three youngest domes in the complex. In addition, seventeen CO2 flux measurements over a thermal water pool were performed. Our findings indicate that the diffuse emission at Pululahua's crater floor is fed by both biogenic and volcanic CO2. Fluxes from each source are similar in magnitude, with approximately 90% of the measurements falling into an intermediate flux range. The occurrence of volcanic CO2 emissions is supported by the carbon isotopic composition. Diffuse degassing distribution highlights a CO2 anomaly surrounding the younger domes within the crater. We estimated the CO2 diffuse emission using both statistical and geostatistical approaches over area of 3.36 km2, resulting in values of 154.2 t d−1 and 126.2 t d−1 respectively. Based on the geostatistical quantification of the total CO2 emission from soil degassing, Pululahua's crater volcanic CO2 contribution is estimated between 59 and 97 t d−1. Finally, the potential hazards associated with the release of cold CO2 at Pululahua's crater are also discussed.

Assessing landslide susceptibility in northern Morocco: A geostatistical mapping approach in Al Hoceima-Ajdir

Landslides are a significant global risk due to their impact on nature, society, and the economy. The Al Hoceima-Ajdir region is highly susceptible to geohazards such as earthquakes and ground instabilities caused by local tectonic movements and specific climatic and geomorphological conditions. This study aims to assess landslide susceptibility in Al Hoceima-Ajdir using a digital mapping approach that integrates the geostatistical Frequency Ratio tool, geomatic tools, and field observations. The frequency ratio method, a widely used technique in geospatial analysis, evaluates the relative frequency of landslides based on the spatial distribution of conditioning factors such as slope, lithology and land use – land cover. This method calculates the ratio of the frequency of landslides within specific classes of these factors to their respective frequencies in the entire study area. This research introduces a novel susceptibility map highlighting major threats to several natural and touristic sites crucial to the region's socio-economic activities. The susceptibility map was developed by analyzing 40 historical landslides along various predisposing factors, including topographic, geo-hydrological, and anthropogenic parameters. The results identify areas prone to landslides and classify their susceptibility into five levels ranging from very high to very low, with each level indicating a different degree of risk and requiring a specific set of preventive measures. The reliability of this map was evaluated using the Receiver Operating Characteristics (ROC) method, demonstrating a high accuracy of 89%, further validated by field visits. This high-precision susceptibility map is significant for pre- and post-risk management, enhancing prevention and mitigation strategies. It also protects society, the economy, and the environment by providing a reliable regional planning and hazard management tool. The findings of this study offer a significant advancement in the methodology for landslide susceptibility assessment and present a model that can be adapted to other regions facing similar geohazards. The research underscores the importance of integrating geostatistical tools with field data to produce accurate and actionable susceptibility maps, benefitting the global scientific community by improving hazard prediction and management strategies.

Ecological Evaluation of Land Resources in the Yangtze River Delta Region by Remote Sensing Observation

The evaluation of land ecological security (LES) evaluates how human activity and land use affect land ecosystems. Its ultimate objective is to provide guidance and assistance for decision making in order to preserve and restore the efficacy and health of terrestrial ecosystems. The assessment model presented in this article is comprehensive and integrates the advantages of both subjective and objective weighting techniques. This study extends the “Pressure–State–Response” (PSR) model to “Driver–Pressure–State-Impact–Response” (DPSIR) and combines it with TOPSISI to determine the weights of each contributing component. Furthermore, the geographical and temporal distribution patterns of regional land ecological security levels were investigated using GIS geostatistical approaches. According to this study, (1) the Yangtze River Delta region’s LES index, with a mean value in the fairly safe range, is generally safe. The year 2019 marks an inflection point for the index, with the highest level of ecological safety on land. The primary element is the modification of environmental policies that are enacted by the government. (2) The LES status is divided into two stages during the course of this study. The Yangtze River Delta region’s LES quickly develops throughout the first stage (2012–2019), which sees a shift in the safety rating from IV to II. The second stage (2019–2023) sees a progressive improvement in the LES index and a shift in the safety category from Class II to Class I. (3) Important variables influencing the geographical distribution of LES in the Yangtze River Delta region include barrier elements, including soil and water erosion areas, flood disaster areas, grain planting areas, urban green covering areas, and effective irrigation areas of farmland.

Enhancing Forest Site Classification in Northwest Portugal: A Geostatistical Approach Employing Cokriging

Enhancing Forest Site Classification in Northwest Portugal: A Geostatistical Approach Employing Cokriging

Assessing the Impact of Climate Change on Glacial Lake Outburst Flood (GLOF) in Eastern Hindu Kush Region Using Integrated Geo-Statistical and Spatial Hydrological Approach

Glacier retreat, a major impact of climate change that continues to occur in many parts of the world, continues to increase the risk of glacial lake outburst floods (GLOFs) in northern Pakistan. The rapid melting of glaciers in the mountains of Northern Pakistan, including the Hindu Kush, the Himalayas and the Karakoram, the rapid melting of glacier has led to the formation of 3044 glacial lakes, with 33 identified as particularly vulnerable to GLOFs. This study uses remote sensing and geographic information systems (GIS) methods for mapping and representing GLOFs. Based on the observational data of lake area, volume, and depth, empirical equations are developed through statistical methods. Only two lakes, Chitral-GL2 and Swat-G31, are classified as lakes with high potential for GLOF. Through modeling techniques using HEC-RAS and HEC-GeoRAS spatial hydrological models integrated with GIS remote sensing, the spatial extent and depth of inundations under different lake volumes are assessed. The analysis reveals that a total area of 20.56 km2 is susceptible to submersion by GLOFs, with Chitral-GL2 flooding area of 14.80 km2 and Swat-GL31 5.79 km2. Different land types are impacted by critical water depths, with built-up and agricultural lands 2.7 km2 totally, and barren lands 8.93 km2 under different flood depths ranging from less than 5 m to over 15 m.

Unravelling factors influencing demand for modern contraception and evaluating coverage progress since 2015 in Ethiopia, Kenya, and Nigeria: insights from multilevel and geostatistical modelling

IntroductionThe United Nations established the Sustainable Development Goals (SDGs) in 2015 to enhance global development. In this study, we examine an SDG indicator: the percentage of women aged 15–49 whose family planning needs are met by modern contraception (mDFPS). We evaluate both the factors influencing its coverage and its progress since 2015.MethodsWe used nationally representative surveys data (Demographic and Health Surveys (DHS) and Performance Monitoring for Action (PMA)) from Ethiopia, Kenya, and Nigeria. We assessed predictors of mDFPS. We also computed mDFPS coverage across countries and subnational areas, assessing coverage changes from the SDGs onset to the most recent period, using a Bayesian model-based geostatistical approach. We assessed whether the subnational areas exceeded the minimum recommended WHO mDFPS coverage of 75%.ResultsVaried individual and community-level determinants emerged, highlighting the countries’ uniqueness. Factors such as being part of a female-headed household, and low household wealth, lowered the odds of mDFPS, while rural-residence had low odds only in Ethiopia and Nigeria. The results indicate mDFPS stagnation in most administrative areas across the three countries. Geographic disparities persisted over time, favouring affluent regions. The predicted posterior proportion of mDFPS and exceedance probability (EP) for WHO target for Ethiopia was 39.85% (95% CI: [4.51, 83.01], EP = 0.08) in 2016 and 46.28% (95% CI: [7.15, 85.99], EP = 0.13) in 2019. In Kenya, the adjusted predicted proportion for 2014 was 30.19% (95% CI: [2.59, 80.24], EP = 0.06) and 44.16% (95%CI: [9.35, 80.24], EP = 0.13) in 2022. In Nigeria, the predicted posterior proportion of mDFPS was 17.91% (95% CI: [1.24, 61.29], EP = 0.00) in 2013, and it was 23.08% (95% CI: [1.80, 56.24], EP = 0.00) in 2018. None of the sub-national areas in Ethiopia and Nigeria exceeded the WHO target. While 9 out of 47 counties in Kenya in 2022 exceeded the WHO mDFPS target.ConclusionThe study unveils demographic, geographic, and socioeconomic mDFPS disparities, signalling progress and stagnation across administrative areas. The findings offer policymakers and governments insights into targeting interventions for enhanced mDFPS coverage. Context-specific strategies can address local needs, aiding SDG attainment.

Prediction of grain size distribution using ordinary kriging and compositional kriging methods

Grain size analysis plays a crucial role in understanding the geological characteristics of the coastal environments that influence the optimizations for oil and gas production operations. This paper aims to explore a sophisticated geostatistical approach using the ordinary kriging and compositional kriging techniques, to forecast the grain size fluctuations of sediments in the Long Island region located in the United States. In addition, utilizing a comprehensive dataset collected from the same region about an integrated seventeen compositional components for investigation using the spatial model of the grain size distribution. Moreover, a variogram and the scatter plot predicted a distinctive spatial dependency was achieved. The compositional kriging method used to predict the grain size distribution in the coastal areas presented an accurate result based on the shape of the histogram, Root Mean Square Error (RMSE), and the Mean Squared Error (MSE). In conclusion, the geo-statistics assisted in the integration of the sedimentological analysis in the coastal settings and showed an effective configuration for the decision-making in the oil and gas industry business.