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Sparse grids: a new predictive modelling method for the analysis of geographic data / S.W. Laffan in International journal of geographical information science IJGIS, vol 19 n° 3 (march 2005)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 19 n° 3 (march 2005) . - pp 267 - 292 Titre : Sparse grids: a new predictive modelling method for the analysis of geographic data Type de document : Article/Communication Auteurs : S.W. Laffan, Auteur ; O.M. Nielsen, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 267 - 292 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] Australie [Termes IGN] classification par réseau neuronal [Termes IGN] exploration de données géographiques [Termes IGN] géomorphométrie [Termes IGN] grille aérée [Termes IGN] morphologie mathématique [Termes IGN] prédiction Résumé : (Auteur) We introduce in this paper a new predictive modelling method to analyse geographic data known as sparse grids. The sparse grids method has been developed for data-mining applications. It is a machine-learning approach to data analysis and has great applicability to the analysis and understanding of geographic data and processes. Sparse grids are a subset of grid-based predictive modelling approaches. The advantages they have over other grid-based methods are that they use fewer parameters and are less susceptible to the curse of dimensionality. These mean that they can be applied to many geographic problems and are readily adapted to the analysis of geographically local samples. We demonstrate the utility of the sparse grids system using a large and spatially extensive data set of regolith samples from Weipa, Australia. We apply both global and local analyses to find relationships between the regolith data and a set of geomorphometric, hydrologic and spectral variables. The results of the global analyses are much better than those generated using an artificial neural network, and the local analysis results are better than those generated using moving window regression for the same analysis window size. The sparse grids system provides a potentially powerful tool for the analysis and understanding of geographic processes and relationships. Numéro de notice : A2005-076 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810512331319118 En ligne : https://doi.org/10.1080/13658810512331319118 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27214 [article]

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