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Artificial neural networks as a tool for spatial interpolation / J.P. Rigol in International journal of geographical information science IJGIS, vol 15 n° 4 (june 2001)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 15 n° 4 (june 2001) . - pp 323 - 343 Titre : Artificial neural networks as a tool for spatial interpolation Type de document : Article/Communication Auteurs : J.P. Rigol, Auteur ; C.H. Jarvis, Auteur ; N. Stuart, Auteur Année de publication : 2001 Article en page(s) : pp 323 - 343 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] covariance [Termes IGN] interpolation [Termes IGN] krigeage [Termes IGN] modèle de simulation [Termes IGN] prédiction [Termes IGN] réseau neuronal artificiel [Termes IGN] température de l'air Résumé : (Auteur) This paper describes the spatial interpolation of daily minimum air temperature using a feedforward backpropagation neural network. Simple network configurations were trained to predict minimum temperature using as inputs: (1) date and terrain variables; (2) temperature observations at a number of neighbouring locations; (3) date, terrain variables and neighbouring temperature observations. This is the first time that trend and spatial association are explicitly considered together when interpolating using a neural network. The internal weights given to different inputs to the network were analysed to estimate the degree of spatial correlation between neighbouring stations in addition to the most influential variables contributing to the underlying trend. The spatial distribution of daily minimum temperature was estimated with the greatest accuracy by a network trained on the most comprehensive data set (3). The best model for the prediction of temperature accounts for 93% of the variance, measured by the correlation between independent estimated and observed values over a full year. This is comparable to accuracies reported in the literature using other approaches such as ordinary kriging of the residuals of multi-variate linear regression or partial thin plate splines. An advantage of this method is that the guiding variables are not assumed necessarily to be linearly related with the data being interpolated, and combinative effects are taken into account. Analysis of the internal network weights confirms that the networks are able to select adaptively between trend and covariance components of the interpolation function. Example interpolated daily minimum temperature surfaces for a 100 km x 100 km area in Yorkshire, UK, were generated using the selected network architectures to illustrate the results achievable with an ANN. Numéro de notice : A2001-041 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810110038951 En ligne : https://doi.org/10.1080/13658810110038951 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21743 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-01041	RAB	Revue	Centre de documentation	En réserve L003	Disponible