Abstract

Modeling of spatio-temporal processes is critical in many fields such as environmental sciences, meteorology, hydrology and reservoir engineering. Nowadays spatio-temporal analysis cannot be adequately faced without considering important issues, such as: (a) modeling the spatio-temporal random field from which data might be reasonably derived, (b) choosing suitable covariance models which describe the spatio-temporal correlation of the variables of interest, (c) using adequate software packages which tackle different inferential problems. In this paper, the above aspects are properly analyzed. In particular, three different space–time random field decomposition choices are considered and the flexibility of using the generalized product–sum model is highlighted. A customized GSLib routine for kriging in space-time is proposed. This Fortran routine, named “K2ST”, is based on the use of the generalized product -sum model, with nested structures, and appropriate space-time search neighborhoods. An application to NO2 pollutant in an urban area is presented. In order to compare kriging results associated with three hypotheses of space–time random field decomposition, correlation coefficients and standardized errors between true values and predicted ones are computed. Moreover, nonparametric tests are applied to check the significance of the difference among the three approaches.

Autore Pugliese

• Posa Donato , De Iaco Sandra

Tutti gli autori

• De Iaco S. , Posa D.

Titolo volume/Rivista

COMPUTERS & GEOSCIENCES

Anno di pubblicazione

2012

ISSN

0098-3004

ISBN

Non Disponibile

Numero di citazioni Wos

14

Ultimo Aggiornamento Citazioni

28/04/2018

Numero di citazioni Scopus

15

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile