Results 41 to 50 of about 6,324 (226)
, 1980 Kriging is a means of spatial prediction that can be used for soil properties. It is a form of weighted local averaging. It is optimal in the sense that it provides estimates of values at unrecorded places without bias and with minimum and known variance.
Webster, R., Burgess, T. M.
core +1 more source
, 2022 This is a data set that contains world-wide estimates of variogram of covariancve functions for aquifer and soil ...
Falk Heße
core +1 more source
Hydrology, 2019 In Eastern Canada, the snow survey network is highly optimized at the operational scale. However, it is commonly accepted that the network is limited when it comes to studying the spatial variability of the snow water equivalent (SWE), which forms ...
Yawu Noumonvi Sena +3 more
doaj +1 more source
Checking Process Stability with the Variogram [PDF]
Quality Engineering, 2005 Modern quality control methods are increasingly being used to monitor complex industrial processes. A key requirement for such methods is the derivation of long records.
Bisgaard, S., Kulahci, M.
openaire +2 more sources
Quarterly Journal of the Royal Meteorological Society, EarlyView.This study develops a method to identify the source areas of precipitation events, as illustrated for the western part of the Netherlands. Radar‐based precipitation data are traced back to their source areas and machine‐learning techniques are used to identify hypothesized causes: urban heat, surface roughness, and air pollution. We find that urban and
Jelmer van der Graaff +1 more
wiley +1 more source
Frontiers in Earth ScienceExperimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) is a new and advanced way of automating experimental variogram modelling.
Saâd Soulaimani +6 more
doaj +1 more source
A geostatistical approach for predicting the top producing formation in oil fields
Geodesy and Cartography, 2012 Drilling engineer's understanding of the subsurface conditions of oil-rich regions in Iran is based on experience and through quantitative assessment of these valuable data.
Mohammad Abdideh, Majed Abyat
doaj +1 more source
Bivariate postprocessing of wind vectors
Quarterly Journal of the Royal Meteorological Society, EarlyView.We introduce three novel bivariate postprocessing approaches and analyze their performance for joint postprocessing of bivariate wind‐vector components in Germany. Bivariate vine‐copula‐based models, a bivariate gradient‐boosted version of ensemble model output statistics (EMOS), and a bivariate distributional regression network (DRN) are compared with
Ferdinand Buchner +3 more
wiley +1 more source
Deducing window size and weights of Parzen classifier, using variogram function
Machine Learning. EngineeringThe Parzen window classifier assigns a class label to an unlabeled observation based on the similarity to known observations (majority voting). This method requires the optimization of two parameters: the Parzen window radius and kernel function.
Pedram Masoudi
doaj +1 more source
Incorporating environmental DNA metabarcoding for improved benthic biodiversity and habitat mapping
Remote Sensing in Ecology and Conservation, EarlyView.Seafloor imagery is commonly used to collect information about the distribution of benthic organisms in order to generate habitat and biodiversity maps. Recent advances in genomics (e.g., environmental DNA; eDNA) show potential to complement video surveys for habitat mapping, but there have been few examples testing this.
Rylan J. Command +8 more
wiley +1 more source