Rainfall data is useful in many fields such as urban management, agriculture, and so on. Spatial interpolation is widely used to interpolation continue rainfall data from discrete rainfall gauges. The uncertainty in spatial interpolation is change in different region. Paper focus on urban small area of Beijing city, Xicheng District and analyses uncertainty of spatial interpolation from four as...

This paper applies a novel Kriging model to the interpolation of stochastic simulation with high computational expense. The novel Kriging model is developed by using Taylor expansion to construct a drift function for Kriging, thus named Taylor Kriging. The interpolation capability of Taylor Kriging for stochastic simulation is empirically compared with those of Simple Kriging and Ordinary Krigi...

A standard survey of soil salinity in Iran has produced a classified soil salinity map and a data set of about 600 electrical conductivity (EC) measurements of the saturated paste extract determined at three depth intervals (050cm, 50-100cm and 100-150cm). However, since the EC values ranged from 1 to 109 mS/cm, a more detailed quantitative evaluation was desired. The study area covers about 45...

The benefits of an integrated geographical information system (GIS) and a geostatistics approach to accurately model the spatial distribution pattern of precipitation are known. However, the determination of the most appropriate geostatistical algorithm for each case is usually neglected, i.e. it is important to select the best interpolation technique for each study area to obtain accurate resu...

This paper demonstrates the application of correlated Gaussian process based approximations to optimization where multiple levels of analysis are available, using an extension to the geostatistical method of co-kriging. An exchange algorithm is used to choose which points of the search space to sample within each level of analysis. The derivation of the co-kriging equations is presented in an i...

Wheat yield and quality parameters are spatially variable because of inherent spatial variability of factors affecting crop at a field scale. The following semolina quality parameters were analyzed in 100 georeferenced locations, in a 12-ha durum wheat field in southern Italy: protein content (PC, %), dough strength (W = Jx10 ) and tenacity/extensibility ratio (P/L). The study identified few ho...

In spatial data modelling and analysis there are a variety of techniques to perform prediction. The goal of these techniques is to take spatially located data and to establish estimates of data values at unknown locations. Of these techniques, the attractive aspects of kriging are often overshadowed by the slow speed of the calculation. Unfortunately the calculations necessary to perform krigin...

We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O’Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with different levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1) Co-kriging) and deep neural networks enables us to construct a method that is immune to disco...