The present work deals with the first application of Support Vector Regression (SVR) for the spatial data mapping. SVR is a recent development of the Statistical Learning Theory (VapnikChervonenkis theory). It is based on Structural Risk Minimisation and seems to be promising approach for the spatial data analysis and processing. There are several attractive properties of the SVR: robustness of...

$EVWUDFW The work is devoted to an application of artificial neural network (multilayer perceptron) and conditional stochastic simulations to electricity load forecasting in Russia. One of the problems is missing data and some important weather parameters (wind, cloudiness, precipitation, historical information). This gives rise to rather large forecasting errors with complex statistical struct...

Accurate prediction of extreme flow events is important for mitigating natural disasters such as flooding. We explore and refine two modelling approaches (both separately in combination) that have been demonstrated to improve the daily peak events. These are firstly, models aggregate fine resolution (sub-daily) simulated from a process-based model (PBM) daily, secondly, hybrid combine PBMs with...

The report presents a series of numerical experiments concerning application of Support Vector Machines for the two class spatial data classification. The main attention is paid to the variability of the results by changing hyperparameters: bandwidth of the radial basis function kernel and C parameter. Training error, testing error and number of support vectors are plotted against hyperparamete...

Abstract The definition of an index to synthesize the tourism appeal a holiday destination is complex due effect different aspects, such as economic, socio-demographic, cultural, geographical ones regarding both demand and supply-side. In this paper, several spatially-referenced factors, related attractiveness, are analyzed through geographically weighted principal components analysis (GWPCA). ...

This study proposes a novel measurement-based method to predict and model three-dimensional (3-D) path loss in indoor scenarios, which first regresses 28 GHz measurements via median modeling then includes ordinary Kriging interpolate shadowing. The performance of this is evaluated by investigating the spatial structure that follows shadowing through semivariogram, covariance function, correlogr...

Spatio-temporal analyses of yield variability are required to delineate areas of stable yield patterns for application of precision farming techniques. Spatial structure and temporal stability patterns were studied using 19951997 yield data for a 25-ha field located near Story City, Iowa. Corn was grown during 1995-1996, and soybean in 1997. The yield data were collected on nine east-west trans...