Spatially constrained regionalization with multilayer perceptron

In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on multivariate homogeneity measures. The study focuses on non‐stationarity and autocorrelation in spatial data. Supervised MLP machine learning al...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Transactions in GIS 2019-10, Vol.23 (5), p.1048-1077
Hauptverfasser:	Govorov, Michael, Beconytė, Giedrė, Gienko, Gennady, Putrenko, Viktor
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Autocorrelation Classification Constraint modelling Datasets Geological surveys Geostatistics Learning algorithms Machine learning Multilayer perceptrons Multivariate analysis Spatial data
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!