Whenever simulation requires much computer time, interpolation is needed. There are several interpolation techniques in use (for example, linear regression), but this paper focuses on Kriging. This technique was originally developed in geostatistics by D. G. Krige, and has recently been widely applied in deterministic simulation. This paper, however, focuses on random or stochastic simulation. ...

In this study, the seasonal rainfall distribution in Türkiye and its 25 main watersheds were estimated, potentials calculated analyzed. Empirical Bayesian kriging (EBK) ordinary (OK) methods applied interpolations. The calculations made through EBK, which provided highest estimation accuracy all seasons. winter, is season with rainfall, Türkiye’s depth 208.8 mm, volume 162.87 billion m3. summer...

Accurate interpolation when compiling bathymetric maps is essential in any water depth study. In the case of Saldanha Bay, continuous dredging operations are constantly altering ocean floor, which has a detrimental effect on sedimentation and coastal hydrodynamics. If integrity coastline to be secured, accurate bathymetry predictions would invaluable determining erosion. Inverse distance weight...

It is well known that climate and circulation model simulation output are often systematically biased. However, existing bias correction methods typically ignore spatial autocorrelation of the biases correct only overall mean variance, resulting in mismatched variability between bias-corrected simulations observations. In this study, we propose using regression kriging (RK) to for biased patter...

Kriging-based exploration strategies often rely on a single Ordinary Kriging model which parametric covariance kernel is selected a priori or on the basis of an initial data set. Since choosing an unadapted kernel can radically harm the results, we wish to reduce the risk of model misspecification. Here we consider the simultaneous use of multiple kernels within Kriging. We give the equations o...

Accurately quantifying soil organic carbon (SOC) stocks in soils is considered necessary and important for studying the soil quality and productivity, modeling the global carbon cycle, and assessing the global climate change. The objectives of this chapter are (1) to evaluate the effects of sampling density and interpolation methods on spatial distribution of SOC density (SOCD) and (2) to estim...

The density of soil observations is a major determinant digital mapping (DSM) prediction accuracy. In this study, we investigated the effect sampling on performance DSM to predict topsoil particle-size distribution in Mayenne region France. We tested two algorithms, namely ordinary kriging (OK) and quantile random forest (QRF). study area ~5000 km2 with highest field France (1 profile per 0.64 ...

The spatial pattern of precipitation is known to be highly dependent on meteorological conditions and relief. However, the relationships between precipitation and topography in mountainous areas are not very well known, partly because of the complex topography in these regions, and partly because of the sparsity of information available to study such relationships in high elevation areas. The p...

the estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. in this paper, we investigate the spatial distribution of three air pollutants in tehran’s atmosphere: carbon monoxide (co), nitrogen dioxide (no2), and atmospheric particulate matters less ...

Machine learning methods, like random forest (RF), have shown their superior performance in various disciplines, but have not been previously applied to the spatial interpolation of environmental variables. In this study, we compared the performance of 23 methods, including RF, support vector machine (SVM), ordinary kriging (OK), inverse distance squared (IDS), and their combinations (i.e., RFO...