Empirical Bayesian kriging implementation and usage

2020 ◽  
Vol 722 ◽  
pp. 137290 ◽  
Author(s):  
Alexander Gribov ◽  
Konstantin Krivoruchko
Keyword(s):  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging

Testing statistical methods to predict pesticide drift deposition

2020 ◽  
Author(s):  
Glenda Garcia-Santos ◽  
Michael Scheiber ◽  
Juergen Pilz
Keyword(s):  
Risk Assessment ◽  
Spatial Interpolation ◽  
Soil Surface ◽  
Farming Systems ◽  
Linear Interpolation ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Spray Technique ◽  
Pesticide Drift

<p><span>We studied the case of the Andean </span><span>region in Colombia as example of non-mechanized small farming systems in which farmers </span><span>use handheld sprayers to spray pesticides. This is the most common </span>technique to spray <span>pesticide in developing countries. To better understand the spatial distribution of</span> airborne pesticide drift deposits<span> on the soil surface using that spray technique, nine different spatial interpolation </span><span>methods were tested using a surrogate tracer substance (Uranine) i.e. classical approaches </span><span>like the linear interpolation and kriging, and some advanced methods like spatial vine </span><span>copulas, the Karhunen-Loève expansion of the underlying random field, the integrated </span><span>nested Laplace approximation and the Empirical Bayesian Kriging used in ArcMap (GIS). </span><span>This study contributes to</span><span> future </span><span>studies on mass balance and risk assessment related to </span>environmental <span>drift pollution in developing </span><span>countries.</span></p>

Geospatial Situation Analysis for the Prediction of Possible Cases of Suicide Using EBK

2021 ◽  
pp. 327-346
Author(s):  
Carlos Manuel Ramirez López ◽  
Martín Montes Rivera ◽  
Alberto Ochoa ◽  
Julio César Ponce Gallegos ◽  
José Eder Guzmán Mendoza
Keyword(s):  
Suicide Prevention ◽  
Interpolation Method ◽  
Error Prediction ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Completed Suicide ◽  
Geostatistical Interpolation ◽  
Mexican State

This research presents the application of Empirical Bayesian Kriging, a geostatistical interpolation method. The case study is about suicide prevention. The dataset is composed of more than one million records, obtained from the report database of the Emergency Service 911 of the Mexican State of Aguascalientes. The purpose is to get prediction surfaces, probability, and standard error prediction for completed suicide cases. Here, the variations in the environment of suicide cases are relative to and dependent on economic, social, and cultural phenomena.

Empirical Bayesian Kriging method to evaluate inter-annual water-table evolution in the Cuenca Alta del Río Laja aquifer, Guanajuato, México

2020 ◽  
Vol 582 ◽  
pp. 124517
Author(s):  
Yanmei Li ◽  
J. Horacio Hernandez ◽  
Manuel Aviles ◽  
Peter S.K. Knappett ◽  
John R. Giardino ◽  
...  
Keyword(s):  
Water Table ◽  
Kriging Method ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Annual Water ◽  
Del Rio

scholarly journals Using Supervised Machine Learning and Empirical Bayesian Kriging to reveal Correlates and Patterns of COVID-19 Disease outbreak in sub-Saharan Africa: Exploratory Data Analysis

2020 ◽  
Author(s):  
Amobi Andrew Onovo ◽  
Akinyemi Atobatele ◽  
Abiye Kalaiwo ◽  
Christopher Obanubi ◽  
Ezekiel James ◽  
...  
Keyword(s):  
Machine Learning ◽  
Positive Impact ◽  
Disease Outbreak ◽  
Rate Of Change ◽  
Sub Saharan Africa ◽  
Supervised Machine Learning ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Sub Saharan

AbstractIntroductionCoronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan1,2, China, and has subsequently spread worldwide. Knowledge of coronavirus-related risk factors can help countries build more systematic and successful responses to COVID-19 disease outbreak. Here we used Supervised Machine Learning and Empirical Bayesian Kriging (EBK) techniques to reveal correlates and patterns of COVID-19 Disease outbreak in sub-Saharan Africa (SSA).MethodsWe analyzed time series aggregate data compiled by Johns Hopkins University on the outbreak of COVID-19 disease across SSA. COVID-19 data was merged with additional data on socio-demographic and health indicator survey data for 39 of SSA’s 48 countries that reported confirmed cases and deaths from coronavirus between February 28, 2020 through March 26, 2020. We used supervised machine learning algorithm, Lasso for variable selection and statistical inference. EBK was used to also create a raster estimating the spatial distribution of COVID-19 disease outbreak.ResultsThe lasso Cross-fit partialing out predictive model ascertained seven variables significantly associated with the risk of coronavirus infection (i.e. new HIV infections among pediatric, adolescent, and middle-aged adult PLHIV, time (days), pneumococcal conjugate-based vaccine, incidence of malaria and diarrhea treatment). Our study indicates, the doubling time in new coronavirus cases was 3 days. The steady three-day decrease in coronavirus outbreak rate of change (ROC) from 37% on March 23, 2020 to 23% on March 26, 2020 indicates the positive impact of countries’ steps to stymie the outbreak. The interpolated maps show that coronavirus is rising every day and appears to be severely confined in South Africa. In the West African region (i.e. Burkina Faso, Ghana, Senegal, Cote d’Iviore, Cameroon, and Nigeria), we predict that new cases and deaths from the virus are most likely to increase.InterpretationIntegrated and efficiently delivered interventions to reduce HIV, pneumonia, malaria and diarrhea, are essential to accelerating global health efforts. Scaling up screening and increasing COVID-19 testing capacity across SSA countries can help provide better understanding on how the pandemic is progressing and possibly ensure a sustained decline in the ROC of coronavirus outbreak.FundingAuthors were wholly responsible for the costs of data collation and analysis.

Evaluation of empirical Bayesian kriging

2019 ◽  
Vol 32 ◽  
pp. 100368 ◽  
Author(s):  
Konstantin Krivoruchko ◽  
Alexander Gribov
Keyword(s):  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging

scholarly journals Assessment of Spatial Prediction Techniques Accuracy for Elevation Determination in Akure South Local Government, Ondo State

2020 ◽  
Vol 5 (5) ◽  
pp. 550-553
Author(s):  
Victor Ayodele Ijaware ◽  
Adebayo T. Adeboye
Keyword(s):  
Polynomial Interpolation ◽  
Inverse Distance Weighting ◽  
Universal Kriging ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Local Polynomial ◽  
Distance Weighting ◽  
Elevation Data ◽  
Inverse Distance

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI), with the collaboration of scientific and industry organizations in both countries. The ASTER instrument provides a more robust remote sensing imaging capability when compared to the older Landsat Thematic Mapper. This paper deals with the accuracy assessment of elevation data obtained using ASTER from each of the eleven (11) selected extrapolation/interpolation algorithms: Inverse Distance Weighting, Natural Neighbor, Spline Regular, Spline Tension, Universal Kriging, Empirical Bayesian Kriging, Topo to Raster, global (trend surface), local polynomial, kernel interpolation with barriers and radial basis functions in Digital Elevation Model (DEM) surface creation. The data were compared with reference to ground control points of differential GPS measurements in the study area. The error statistics were generated between DGPS measurements and Extracted elevation data from each selected interpolation method. It was observed that Spline Regular Interpolation shown the best overall accuracy of ±11.520m when elevation data extracted from Inverse distance weighting, Natural Neighbour, Spline T, Topo to Raster, Universal Kriging, Empirical Bayesian kriging, Global polynomial interpolation (GPI), local polynomial interpolation (LPI), Radial basis function and Kernel interpolation of ±15.170, ±14.340, ±12.336, ±13.551, ±14.707, ±13.711, ±15.363, ±13.964, ±13.590 and ±15.376 respectively when compared with elevation values from GPS method. The study recommends capacity building in the form of workshop, training, and flexible integration of point elevation data to DEM.

scholarly journals Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM2.5 Concentration in Central and Southern China

2021 ◽  
Vol 18 (15) ◽  
pp. 7931
Author(s):  
Pengzhi Wei ◽  
Shaofeng Xie ◽  
Liangke Huang ◽  
Lilong Liu
Keyword(s):  
Geographically Weighted Regression ◽  
Southern China ◽  
Central China ◽  
Weighted Regression ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
South Central ◽  
Interpolation Accuracy ◽  
Average Improvement

With the increasing application of global navigation satellite system (GNSS) technology in the field of meteorology, satellite-derived zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data have been used to explore the spatial coverage pattern of PM2.5 concentrations. In this study, the PM2.5 concentration data obtained from 340 PM2.5 ground stations in south-central China were used to analyze the variation patterns of PM2.5 in south-central China at different time periods, and six PM2.5 interpolation models were developed in the region. The spatial and temporal PM2.5 variation patterns in central and southern China were analyzed from the perspectives of time series variations and spatial distribution characteristics, and six types of interpolation models were established in central and southern China. (1) Through correlation analysis, and exploratory regression and geographical detector methods, the correlation analysis of PM2.5-related variables showed that the GNSS-derived PWV and ZTD were negatively correlated with PM2.5, and that their significances and contributions to the spatial analysis were good. (2) Three types of suitable variable combinations were selected for modeling through a collinearity diagnosis, and six types of models (geographically weighted regression (GWR), geographically weighted regression kriging (GWRK), geographically weighted regression—empirical bayesian kriging (GWR-EBK), multiscale geographically weighted regression (MGWR), multiscale geographically weighted regression kriging (MGWRK), and multiscale geographically weighted regression—empirical bayesian kriging (MGWR-EBK)) were constructed. The overall R2 of the GWR-EBK model construction was the best (annual: 0.962, winter: 0.966, spring: 0.926, summer: 0.873, and autumn: 0.908), and the interpolation accuracy of the GWR-EBK model constructed by inputting ZTD was the best overall, with an average RMSE of 3.22 μg/m3 recorded, while the GWR-EBK model constructed by inputting PWV had the highest interpolation accuracy in winter, with an RMSE of 4.5 μg/m3 recorded; these values were 2.17% and 4.26% higher than the RMSE values of the other two types of models (ZTD and temperature) in winter, respectively. (3) The introduction of the empirical Bayesian kriging method to interpolate the residuals of the models (GWR and MGWR) and to then correct the original interpolation results of the models was the most effective, and the accuracy improvement percentage was better than that of the ordinary kriging method. The average improvement ratios of the GWRK and GWR-EBK models compared with that of the GWR model were 5.04% and 14.74%, respectively, and the average improvement ratios of the MGWRK and MGWR-EBK models compared with that of the MGWR model were 2.79% and 12.66%, respectively. (4) Elevation intervals and provinces were classified, and the influence of the elevation and the spatial distribution of the plane on the accuracy of the PM2.5 regional model was discussed. The experiments showed that the accuracy of the constructed regional model decreased as the elevation increased. The accuracies of the models in representing Henan, Hubei and Hunan provinces were lower than those of the models in representing Guangdong and Guangxi provinces.

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