Ordinary Kriging Method Research Articles

Spatial distribution, and predictors of late initiation of first antenatal care visit in Ethiopia: Spatial and multilevel analysis.

Despite the proven benefit of early initiation of first antenatal care visits as a means to achieve good maternal and neonatal health outcomes through early detection and prevention of risks during pregnancy, shreds of evidence showed that most of the women in Ethiopia start their ANC visits lately. To determine the spatial distribution and predictors of late initiation of first antenatal care visits among reproductive-age women in Ethiopia. The 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data were used. A total weighted sample of 2,935 reproductive-age women who gave birth in the five years preceding the survey and who had antenatal care visits for their last child was included. To check the nature of the distribution of late initiation of ANC visits, the global Moran's I statistics were applied. Gettis-OrdGi statistics and spatial interpolation using the Ordinary Kriging method were done to identify the spatial locations and to predict unknown locations of late initiation of first ANC visits, respectively. For the predictors, a multilevel mixed-effect logistic regression model was applied. Finally, statistical significance was declared at a p-value < 0.05. The prevalence of late initiation of first ANC visits in Ethiopia was 62.6%. The spatial analysis showed that the late initiation of first ANC visits significantly varied across regions of Ethiopia. The spatial interpolation predicted the highest rates of late initiation of first ANC visits in the eastern SNNPRs, southern and western Oromia, and some parts of the Somalia region. Being rural residents, attending higher education, having medium wealth status, richer wealth status, richest wealth status, having ≥ 5 family size, a household headed by male, living in SNNPRs, and Oromia regions were significant predictors of late initiation of first ANC visits. A clustered pattern of areas with high rates of late initiation of the first ANC visit was detected in Ethiopia. Public health intervention targeting the identified hotspot areas, and women's empowerment would decrease the late start of the first ANC visit. Furthermore, the identified predictors should be underscored when designing new policies and strategies.

Assessment and Characterization of Groundwater Quality of Malabar Coast in Kerala, India

A study was conducted to assess and characterize the groundwater quality of coastal aquifer of the Vatakara-Koyilandy stretch in the Kozhikode district of Kerala. Mann-Kendall Test was used for analysing the trend in groundwater levels. The Piper diagram was applied to determine the chemical facies of the groundwater and identify the evolution of hydrochemical parameters of groundwater sources. The source of the dissolved ions in the groundwater was described by the Gibbs diagram. The suitability of groundwater for irrigation was determined using the United States Salinity Laboratory diagram. Geostatistical tools were used to describe the spatial variability of groundwater levels and salinity and the ordinary kriging method was used to plot the spatial variability maps. It was found that Na+ was the predominant cation with maximum concentration varying from 455.6 mg/l to 1844 mg/l during pre-monsoon and post-monsoon; respectively. Concentration of Cl- in pre-monsoon and post-monsoon varied from 184 mg/l to 2417 mg/l and 99 mg/l to 714 mg/l; respectively. Other anions in the groundwater were SO42- and HCO3- with an average concentration of 239.79 mg/l and 129.59 mg/l in pre-monsoon and 110.73 mg/l and 75 mg/l in post-monsoon; respectively. The trend in groundwater in confined and semi-confined aquifers showed negative trends whereas eight wells resulted in a significantly negative trend. However, a significant decreasing trend was observed in wells near the coasts. The most dominant cations were (Na+, and K+) and the dominant anions were SO42- and Cl-. The dominant cations and anions were from the mixing of seawater with the groundwater. The Na+ ions were also found to be from the same source. In most of the area, the groundwater was highly to very high highly saline with medium sodium and not suitable for irrigation. In the unconfined aquifer, 25.2% and, in the semi-confined aquifer 24.0% area was found to be unfit for irrigation.

Assessment of Spatial Variability of Groundwater Levels in Moroto District, Uganda

Globally, the variation of groundwater levels is increasingly overwhelming due to over exploitation resulting from population growth. The dynamic nature of socio-economic activities in Moroto District such as agriculture, settlements and increased trends in irrigation technology has been well-known worldwide as common parameters triggering groundwater variability. Nevertheless, determination of groundwater levels for groundwater management and development purpose is a challenge due to spatial variability in levels across Moroto District. In this study, geostatistical technique in Geographical Information System (GIS) was used to analyze spatial variability of water levels in the study area. The analysis utilized ordinary Kriging method to predict the spatial variation of groundwater levels for 189 measured boreholes. The resultant groundwater level variation map generated using Gaussian model depicts change in groundwater levels varying significantly across the study area dropping by 12 m to 50 m. Significant variations of groundwater levels were observed in the western, central and south eastern part of the study area which are highly populated areas and therefore, the potential of overexploitation. Generated map can be used as first-hand information for decision makers and water managers in management of groundwater resources in Moroto District.

Integrated processing methodology to identify the spatial variability of accumulated infiltration for agricultural soil

The infiltration of water in the soil, and its variation in space, is essential to establish the irrigation schedule for crops and to evaluate the possible degrading effects on the soil. The objective was to develop an integrated processing methodology in Rstudio to identify the spatial variability of the accumulated infiltration, in two phases related to pea crops. Field sampling was carried out on a rectangular mesh with 48 points per moment, using double infiltrometer rings. The data were evaluated by means of geostatistical tools adjusted with programming code in Rstudio, defining the relationships between the magnitudes of the accumulated infiltration, for different test instants, without the need to make statistical adjustments to the normality of variables, discriminated over a period between 1 and 80 minutes. The results suggest the existence of spatial variability of the accumulated infiltration in the two evaluated phases, considering that most of the analyzed data were adjusted to multiple variance models, maintaining a degree of spatial dependence, and validating the effectiveness of the adjusted methodology developed and implemented. The spatial relationships were corroborated by means of contour maps, where the spatial variation of the accumulated infiltration between the two identified cultivation moments was observed. The reliability of the interpolation by the Ordinary Kriging method was verified by generating variance maps, establishing the degree of homogeneity of the interpolation. The variability of infiltration confirms the validity of the adjusted methodology implemented.

The Relationship between Bedrock Depth and Site Fundamental Frequency in the Nakdonggang Delta Region, South Korea

This paper describes the relationship between bedrock depth (D) and site fundamental frequency (f0) in the Nakdonggang delta region in the southeastern part of the Korean peninsula. We collected borehole logs to confirm the thickness of the sediments and estimated the f0 at over 200 locations across the delta using the horizontal-to-vertical spectral ratio (HVSR) method. We developed an f0 map of the study area by spatially interpolating the f0 values using the Ordinary Kriging method. The bedrock depth in the main delta showed a power-law dependence on the f0. The derived f0–D model predicted much shallower bedrock depths compared with similar studies from other parts of the world. This was attributed to the fact that the Nakdonggang delta region is composed of relatively low Vs Holocene sediments. With an f0 map, the derived model could enable a quick estimation of the bedrock depth, which could help to determine the site class in the Nakdonggang delta region according to the Korean Seismic Design Standard (KDS 17 10 00).

Reconstructing missing data by comparing interpolation techniques: Applications for long‐term water quality data

AbstractMissing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a long‐term water quality dataset from the Upper Mississippi River, U.S.A. The dataset included 80,000 sampling sites collected over 30 yr that had substantial missingness for total nitrogen (TN), total phosphorus (TP), and water velocity. For all three interpolated water quality variables, random forests had very high prediction accuracy and outperformed the methods of ordinary kriging, polynomial regressions, regression trees, and inverse distance weighting. TP had a mean absolute error (MAE) of 0.03 mg (L‐TP)−1, TN had a MAE of 0.39 mg (L‐TN)−1, and water velocity had a MAE of 0.10 m s−1. The random forests' error rates were mapped and showed low spatiotemporal variability across the riverscape, indicating high model performance across many habitat types and large spatial scales. In the current era of “big data,” interpolation becomes an imperative step prior to ecological analyses yet remains unfamiliar and underutilized. Our research briefly describes the importance of addressing missingness and provides a roadmap to conduct model intercomparisons of other big datasets. We also share adaptable data analysis scripts, which allows others to readily conduct interpolation comparisons for many limnology applications and contexts.

A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation

Abstract. Spatial interpolation, a fundamental spatial analysis method, predicts unsampled spatial data from the values of sampled points. Generally, the core of spatial interpolation is fitting spatial weights via spatial correlation. Traditional methods express spatial distances in a conventional Euclidean way and conduct relatively simple spatial weight calculation processes, limiting their ability to fit complex spatial nonlinear characteristics in multidimensional space. To tackle these problems, we developed a generalized spatial distance neural network (GSDNN) unit to generally and adaptively express spatial distances in complex feature space. By combining the spatial autoregressive neural network (SARNN) with the GSDNN unit, we constructed a generalized spatial autoregressive neural network (GSARNN) to perform spatial interpolation in three-dimensional space. The GSARNN model was examined and compared with traditional methods using two three-dimensional cases: a simulated case and a real Argo case. The experiment results demonstrated that exploiting the feature extraction ability of neural networks, the GSARNN achieved superior interpolation performance and was more adaptable than inverse distance weighted, ordinary Kriging, and SARNN methods.

Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network

A quantitative research on the effect of coal mining on the soil organic carbon (SOC) pool at regional scale is beneficial to the scientific management of SOC pools in coal mining areas and the realization of coal low-carbon mining. Moreover, the spatial prediction model of SOC content suitable for coal mining subsidence area is a scientific problem that must be solved. Taking the Changhe River Basin of Jincheng City, Shanxi Province, China, as the study area, this paper proposed a radial basis function neural network model combined with the ordinary kriging method. The model includes topography and vegetation factors, which have large influence on soil properties in mining areas, as input parameters to predict the spatial distribution of SOC in the 0–20 and 2040 cm soil layers of the study area. And comparing the prediction effect with the direct kriging method, the results show that the mean error, the mean absolute error and the root mean square error between the predicted and measured values of SOC content predicted by the radial basis function neural network are lower than those obtained by the direct kriging method. Based on the fitting effect of the predicted and measured values, the R2 obtained by the radial basis artificial neural network are 0.81, 0.70, respectively, higher than the value of 0.44 and 0.36 obtained by the direct kriging method. Therefore, the model combining the artificial neural network and kriging, and considering environmental factors can improve the prediction accuracy of the SOC content in mining areas.

Analysis of the Distribution of Diarrhea Patients in Bogor Regency Month Using the Ordinary Kriging Method

Ordinary kriging is the most widely used kriging method. It serves to estimate a value at a point of a region for which a variogram is known, using data in the neighborhood of the estimation location. Ordinary kriging can also be used to estimate a block value. With local second-order stationarity, ordinary kriging implicitly evaluates the mean in a moving neighborhood. To see this, first a kriging estimate of the local mean is set up, then a simple kriging estimator using this kriged mean is examined.&#x0D; The objective of this study was to determine the pattern of diarrheal disease spread in Bogor Regency. The method according to the purpose of this research is Ordinary Kriging. To know the distribution of diarrhea disease in Bogor Regency by looking at the result of countour plot. Based on the countour plot, the area that has the highest number of diarrhea sufferers around Barekah and Bojonggenteng villages is marked with White contour color with the range of diarrhea sufferer is 40 - 43 people. Areas with the highest number of diarrheal diseases indicate that the area has a relationship with the number of diarrheal diseases in the surrounding area. Therefore, the area needs to be prioritized in improving water sanitation, counseling to the community and improving health services.

Prediction of PM2.5 in DKI Jakarta Using Ordinary Kriging Method

Air pollution is a serious matter that must be addressed promptly and quickly. One of the most dangerous pollutants in the air is PM2.5. This pollutant is particulates dust measuring 2.5 micrometers. PM2.5 can cause environmental and health problems such as acute respiratory infections, lung cancer, cardiovascular cancer, and premature death. Air pollution occurs in big cities such as the capital city of Indonesia, DKI Jakarta, which is the city with the highest PM2.5 levels in Indonesia. There are 6 six stations in DKI Jakarta that measure PM.2.5 level at 6 areas. The ordinary kriging is one of spatial methods that can be used to predict PM2.5 level in outside the existing stations, for example in the Pulogadung industrial area. This area was selected because there are many factories in this area that can increase levels of PM2.5 in the air. To predict the concentration of PM2.5 in one area could be done by calculating the surrounding PM2.5 concentrations that were not available to measure air quality. In study, we use mean an absolute percentage error ( MAPE ) value to evaluate Ordinary Kriging performance for predicting PM2.5 level in DKI Jakarta.

Prediction of Pneumonia Patient Proportions Using Kriging and Inverse Distance Weighted Methods

Pneumonia is a respiratory tract disease caused by bacteria, viruses, or fungi. In Indonesia, pneumonia is one of the diseases with the second highest number of cases after malaria from 2007 to 2015. Mapping of regions with pneumonia prevalence needs to be done so that the government can pay more attention to areas with high pneumonia rates, and the public can be more aware of areas that are prone to pneumonia. Spatial methods that can be used to predict the distribution of pneumonia patients are ordinary kriging and inverse distance weighted. The research results showed that the best method chosen for predicting the proportion of pneumonia patients in Sumatra, Java, Kalimantan, and Sulawesi islands is the ordinary kriging method with its theoretical semivariogram model being exponential, and the obtained RMSE value is 0.0178. The average proportion of pneumonia patients in Sumatra Island is 0.01655, with the highest proportion value of 0.02504 in Simalungun District, North Sumatra. Then, the average proportion of pneumonia patients in Java Island is 0.01672, with the highest proportion value of 0.02443 in Garut District, West Java. The proportion of pneumonia patients in Kalimantan Island is 0.01657, with the highest proportion value of 0.01835 in Balikpapan District, East Kalimantan. The average proportion of pneumonia patients in Sulawesi Island is 0.0166, with the highest proportion value of 0.02397 in South Minahasa District, North Sulawesi.

Spatial distribution and reserve estimation of sand and gravel deposits using geostatistical methods in west Basrah, southern Iraq

Sand and gravel are among the most common natural materials. They are generally used as a raw material in concrete, road construction, mixing with asphalt and filling material, among other uses. Sand and gravel deposits are widespread within the southern and central regions of Iraq. Among these deposits are the alluvial sand and gravel that form the upper part of the Dibdibba Formation in the Al-Rafiya, Al-Tuba, Chwaibda and Safwan blocks in west Basrah, southern Iraq. In this research, grain size distribution for channel samples collected from each block was determined. Thickness of overburden and industrial beds was measured and the stripping ratio (SR) was calculated. Geostatistical interpolation approaches including inverse distance weighting (IDW) and ordinary kriging (OK) were utilised to predict the spatial distribution of sand and gravel deposits and their reserves from borehole data. Data validation using the mean error (ME) and root-mean-square error (RMSE) were applied. The deposits can be characterised as gravelly sand (gS) in the Al-Tuba, Al-Rafiya and Chwaibda blocks, while it is a sandy gravel (sG) in the Safwan block. The overburden gypcrete beds varied between 1 and 3 ​m thick, while the industrial sand and gravel beds which appear as planar and sometimes lenticular deposits ranging between 3 and 8 ​m thick. The SR is generally low, considering the easily removed lightweight material of the overburden bed and the easily extractable fairly flat and near-surface friable sand and gravel deposits. Reserves are estimated as 376.63 Million m3 and 385.97 Million m3 using the IDW and OK approaches, respectively. Based on the least error values of ME and RMSE, we conclude that the OK method is the more accurate spatial interpolation approach for sand and gravel reserve estimation in all blocks compared to the IDW method.

Characterizing groundwater quality for a safe supply of water using WQI and GIS in Bahir Dar city, northwest Ethiopia

Abstract Although both urban and rural residents benefit from drinking enough high-quality water in the right amounts, the degree of contamination from artificial sources has been increasing. The study aims to assess the quality and availability of groundwater potential in Bahir Dar City using geographic information systems (GIS)-based ordinary kriging (OK) and analytical hierarchy process methods, respectively. The concentrations of pH, alkalinity, Escherichia coli, nitrite manganese, and iron in the groundwater of built-up areas were found to exceed the limits set by the World Health Organization. The groundwater quality distribution contained 69.6% of good water, 19.6% of the excellent class, and 10.8% of the poor class. The high potential of groundwater, particularly in the Lake Tana shoreline sedimentation areas, revealed the poor quality class. The results suggest that improving groundwater quality should be prioritized in areas with high potential groundwater availability.

Spatial variability of the granulometric composition within arable field on soddy-podzolic soil

T e spatial variability of the content of granulometric fraction d&gt;0,25 mm in the arable layer of soddy-podzolic cultivated soil on an area of 18 hectares was studied. T e soil formed in the loess-like loam, underlain by moraine deposits. Samples were taken from the upper (0-10 cm) and lower parts of the arable layer (10-20 cm) following a random-stratif ed sampling scheme. T e total number of samples was 350 cores. T e average fraction content was about 22%, the coef cient of variation was 39-41%, the distributions were approximated by a logarithmically normal distribution. T e Spearman correlation coef cient between values at dif erent depths equaled to 0,87. T e spatial distribution cartograms were constructed by the ordinary kriging method using a spherical variogram model. It is shown that preliminary censoring of high sample values (quantile 0,95) gives better results when constructing a cartogram than removing a linear spatial trend and logarithm of the original data. Spatial structures with reduced and increased values were found on the site, the average linear sizes of which was about 100 m. Presumably, they were associated with the heterogeneity of soil-forming material.

The impact of monthly air pollution exposure and its interaction with individual factors: Insight from a large cohort study of comprehensive hospitalizations in Guangzhou area.

Although the association between short-term air pollution exposure and certain hospitalizations has been well documented, evidence on the effect of longer-term (e. g., monthly) air pollution on a comprehensive set of outcomes is still limited. A total of 68,416 people in South China were enrolled and followed up during 2019-2020. Monthly air pollution level was estimated using a validated ordinary Kriging method and assigned to individuals. Time-dependent Cox models were developed to estimate the relationship between monthly PM10 and O3 exposures and the all-cause and cause-specific hospitalizations after adjusting for confounders. The interaction between air pollution and individual factors was also investigated. Overall, each 10 μg/m3 increase in PM10 concentration was associated with a 3.1% (95%CI: 1.3%-4.9%) increment in the risk of all-cause hospitalization. The estimate was even greater following O3 exposure (6.8%, 5.5%-8.2%). Furthermore, each 10 μg/m3 increase in PM10 was associated with a 2.3%-9.1% elevation in all the cause-specific hospitalizations except for those related to respiratory and digestive diseases. The same increment in O3 was relevant to a 4.7%-22.8% elevation in the risk except for respiratory diseases. Additionally, the older individuals tended to be more vulnerable to PM10 exposure (P interaction: 0.002), while the alcohol abused and those with an abnormal BMI were more vulnerable to the impact of O3 (P interaction: 0.052 and 0.011). However, the heavy smokers were less vulnerable to O3 exposure (P interaction: 0.032). We provide comprehensive evidence on the hospitalization hazard of monthly PM10 and O3 exposure and their interaction with individual factors.

Risk for type 2 diabetes mellitus death attributed to insufficient whole grain intake in seven regions of China, 2005-2018

Objective: To estimate the risk for type 2 diabetes mellitus (T2DM) death attributed to insufficient whole grain intake in seven regions of China from 2005 to 2018. Methods: Based on China National Nutrition and Health Surveys and China Adult Chronic Disease and Nutrition Surveillance, ordinary Kriging method and locally weighted regression were used to estimate the level of whole grain intake of Chinese residents from 2005 to 2018. Based on the results of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 and Chronic Diseases Risk Factors Surveillance in China, we calculated the population attributable fraction (PAF), attributable death number and attributable mortality rate of T2DM due to insufficient whole grain intake in people aged ≥20 years in different regions of China, and we used the 2010 Chinese census data to compare the changes in T2DM deaths attributed to insufficient intake of whole grains in seven regions of China. Results: The whole grain intake levels of Chinese people over 20 years old in 2002, 2010 and 2015 were 19.0 g/d, 14.3 g/d and 19.8 g/d, respectively. The estimated overall whole grain intake level was 20.1 g/d in Chinese residents in 2018, and the intake level was 19.4 g/d in men and 20.8 g/d in women. Among the seven regions, the intake level was highest in northern China (47.4 g/d) and lowest in southwestern China (6.0 g/d). In 2018, the PAF was lowest in northern China (12.8%) and highest in southwestern China (19.3%). From 2005 to 2018, the PAF varied in the seven regions, and the PAF in northeastern China fluctuated around 18.5%. Other regions showed downward trends, especially in northern China and northwestern China, decreased by 26.4% and 21.2%, respectively. Over the past 14 years, the number of attributable deaths in the seven regions showed upward trends, with the highest annual average growth rate of 6.7% in southern China and the lowest annual average growth rate of 2.4% in northern China. In 2018, the standardized T2DM mortality rate attributed to insufficient whole grain intake in China was 3.13/100 000, and the attributable mortality was 3.21/100 000 in men and 3.05/100 000 in women. The standardized attributable mortality rate was highest in southwestern China (3.97/100 000) and lowest in northern China (1.78/100 000). From 2005 to 2018, the standardized attributable mortality rate increased by 11.5% in men and decreased by 8.1% in women. The standardized attributable mortality rate in southwestern, southern and central China increased by 23.7%, 21.3% and 4.2%, respectively. The standardized attributable mortality rate in northern, northwestern, eastern and northeastern China decreased by 20.9%, 11.0%, 4.5% and 3.9%, respectively. Conclusion: The whole grain intake level of Chinese residents was low, and the whole grain intake of residents in all seven regions should be increased, especially in the southwest, and men should have more whole grain intake than women to reduce the death risk in patients with T2DM.

High-Resolution Mapping of Soil Organic Matter at the Field Scale Using UAV Hyperspectral Images with a Small Calibration Dataset

The rapid acquisition of high-resolution spatial distribution of soil organic matter (SOM) at the field scale is essential for precision agriculture. The UAV imaging hyperspectral technology, with its high spatial resolution and timeliness, can fill the research gap between ground-based monitoring and remote sensing. This study aimed to test the feasibility of using UAV hyperspectral data (400–1000 nm) with a small-sized calibration sample set for mapping SOM at a 1 m resolution in typical low-relief black soil areas of Northeast China. The experiment was conducted in an approximately 20 ha field. For calibration, 20 samples were collected using a 100 × 100 m grid sampling strategy, while 20 samples were randomly collected for independent validation. UAV captured hyperspectral images with a spatial resolution of 0.05 × 0.05 m. The extracted spectra within every 1 × 1 m were then averaged to represent the spectra of that grid; this procedure was also performed across the whole field. Upon applying various spectral pretreatments, including absorbance conversion, multiple scattering correction, Savitzky–Golay smoothing filtering, and first-order differentiation, the absolute maximum values of the correlation coefficients of the spectra for SOM increased from 0.41 to 0.58. Importance analysis from the optimal random forest (RF) model showed that the characterized bands of SOM were located in the 450–600 and 750–900 nm regions. When the RF model was used, the UAV hyperspectra data (UAV-RF) were able to successfully predict SOM, with an R2 of 0.53 and RMSE of 1.48 g kg−1. The prediction accuracy was then compared with that obtained using ordinary kriging (OK) and the RF model based on proximal sensing (PS-RF) with the same number of calibration samples. However, the OK method failed to predict the SOM accuracy (RMSE = 2.17 g kg−1; R2 = 0.02) due to a low sampling density. The semi-covariance function was unable to describe the spatial variability of SOM effectively. When the sampling density was increased to 50 × 50 m, OK successfully predicted SOM, with RMSE = 1.37 g kg−1 and R2 = 0.59, and its results were comparable to those of UAV-RF. The prediction accuracy of PS-RF was generally consistent with that of UAV-RF, with RMSE values of 1.41 g kg−1 and 1.48 g kg−1 and R2 values of 0.57 and 0.53, respectively, which indicated that SOM prediction based on UAV-RF is feasible. Additionally, compared with the PS platforms, the UAV hyperspectral technology could simultaneously provide spectral information of tens or even hundreds of continuous bands and spatial information at the same time. This study provides a reference for further research and development of UAV hyperspectral techniques for fine-scale SOM mapping using a small number of samples.

Studi Identifikasi Sebaran Ion Logam Pb, Cu, As dan Cd pada Air dan Sedimen di Perairan Bontang Kalimantan Timur Menggunakan Metode Kriging (Ordinary Kriging)

Bontang is one of the cities in East Kalimantan which is geographically located in coastal areas so that it is very vulnerable to marine pollution both from domestic human and industrial activities. This study aims to determine the level and pattern of distribution of poluton metal ions Pb, Cu, As and Cd in water and sediments in Bontang waters using the Kriging (Ordinary Kriging) method. In this study, 4 observation samples were used which were tested in the laboratory to determine the content of heavy metals. Furthermore, the data from laboratory tests along with 4 secondary data were interpolated using the ordinary kriging method with an exponential model to determine the pattern of contour distribution in Bontang waters. The results showed that the heavy metal with the highest distribution value in water and sediment is cadmium with a susceptibility value ranging from 0.006 mg/l to 9.070 mg/l. In general, the pattern of spread of heavy metals originates from coastal areas in a southeasterly direction. The most influential thing on this pattern of spread is tidal currents and bathymetry.

Mining Ground Deformation Estimation Based on Pre-Processed InSAR Open Data—A Norwegian Case Study

Sublevel caving mining causes surface deformation in three distinct zones parallel to the extracted deposit. Most of the published research is focused on the extent of the caved and fracture zones. The extent of the largest, continuous deformation zone and, thus, the influence of the mine on its surroundings is not yet fully documented. This study aimed at assessing the extent of surface deformation caused by the mining of a steep iron ore deposit in Norway. For this purpose, an innovative combination of the permanent scatterer (PS) InSAR technique and line-of-sight (LOS) movement data provided by a public web service and geographic information system (GIS) spatial interpolation methods was proposed. Two ascending tracks’ (A102 and A175) datasets spanning the period of 3 June 2016–11 October 2021 were used. Three interpolation methods, inverse distance weighted (IDW), radial basis function (RBF) and ordinary kriging (OK), were analysed in terms of their performance for mapping continuous deformation. The RBF and OK methods with anisotropy returned the lowest root mean square error (RMSE) values. The obtained difference in the maximum extent of deformation amounted to 26 m for the track A102 dataset and 44.5 m for the track A175 dataset, depending on the interpolation method used. The estimated maximum extent of the continuous deformation zone on the hanging-wall side of the sublevel caving mining operation is 663 m. This corresponds to a limit angle of 38.7 degrees, which is lower than in previously published studies. The results show that the influence of sublevel caving mining on the surroundings can be greater than previously thought. The usefulness of public PSInSAR data available from a national online service and spatial interpolation methods for determining the area of mining terrain deformations has been proven. The proposed approach provides a low-cost alternative and complementation for surveys performed about the mine and it is argued that it should be implemented as part of the mine’s monitoring system.

Improvements in rainfall estimation over Bangkok, Thailand by merging satellite, radar, and gauge rainfall datasets with the geostatistical method

ABSTRACT Bangkok is located in a low land area, and floods frequently occur from rainfall, river discharge, and tides. High-accuracy rainfall data are needed to achieve high-accuracy flood predictions from hydrological models. The main objective of this study is to establish a method that improves the accuracy of precipitation estimates by merging rainfall from three sources: an infrared channel from the Himawari-8 satellite, rain gauges, and ground-based radar observations. This study applied cloud classification and bias correction using rain gauges to discriminate these errors. The bias factors were interpolated using the ordinary kriging (OK) method to fill in the areas of estimated rainfall where no rain gauge was available. The results show that bias correction improved the accuracy of radar and Himawari-8 rainfall estimates before their combination. The merged algorithm was then adopted to produce hourly merged rainfall products (GSR). Compared to the initial estimated product, the GSR is significantly more accurate. The merging algorithm increases the spatial resolution and quality of rainfall estimates and is simple to use. Furthermore, these findings not only reveal the potential and limitations of the merged algorithm but also provide useful information for future retrieval algorithm enhancement.