The precision agriculture hopes to manage the variation in soil nutrient status continuously, which requires reliable predictions at places between sampling sites. For the long time, ordinary kriging has been used as one prediction method when the data are spatially dependent and a suitable variogram model exists. However, even if data are spatially correlated, there are often few soil sampling...

This paper presents a quantitative modeling for delineating alteration zones in the hypogene zone of the Miduk porphyry copper deposit (SE Iran) based on the core drilling data. The main goal of this work was to apply the Ordinary Kriging (OK), Artificial Neural Networks (ANNs), and Concentration-Volume (C-V) fractal modelings on Cu grades to separate different alteration zones. Anisotropy was ...

Abstract This study aims to assess the urban air quality, and map NO 2 , SO PM 10 profiles using two widely spatial interpolation methods, ordinary kriging (OK) inverse distance weighting (IDW) by estimating their concentrations at distinct non-monitored locations. Assessment mapping of pollutants in this help with policy formulation decision-making. Kota, industrial educational hub Rajasthan (...

This study aims to recognize effect of Ordinary Kriging (OK) and Inverse Distance Weighted (IDW) estimation methods for separation of geochemical anomalies based on soil samples using Concentration-Area (C-A) fractal model in Irankuh area, central Iran. Variograms and anisotropic ellipsoid were generated for the Pb and Zn distribution. Thresholds values from the C-A log-log plots based on the e...

The purpose of this study is to identify the effect of Ordinary Kriging (OK) and Inverse Distance Weighted (IDW) estimation methods for the delineation of mineralized zones based on subsurface data using concentration–volume (C–V) multifractal modeling in the Dardevey iron ore deposit, NE Iran. Variograms and anisotropic ellipsoid were generated for the Fe distribution by the above estimation m...

Spatial discontinuity often causes poor accuracy when a single model is used for the surface modeling of soil properties in complex geomorphic areas. Here we present a method for adaptive surface modeling of combined secondary variables to improve prediction accuracy during the interpolation of soil properties (ASM-SP). Using various secondary variables and multiple base interpolation models, A...

Aiming at the tracing problem of ambient air quality exceeding standard caused by unorganized emission multiple pollution sources in chemical industry park, an method based on OK-IGSO integrated algorithm is proposed. First all, ordinary Kriging (OK) geostatistics applied to prediction and interpolation atmospheric pollutants so that distribution closer reality; Then, improved glowworm (IGSO), ...

Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. No...

Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to stu...

Methods of Land Use Regression (LUR) modeling and Ordinary Kriging (OK) interpolation have been widely used to offset the shortcomings of PM2.5 data observed at sparse monitoring sites. However, traditional point-based performance evaluation strategy for these methods remains stagnant, which could cause unreasonable mapping results. To address this challenge, this study employs 'information ent...