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Verfasst von:Shafizadeh Moghaddam, Hossein [VerfasserIn]   i
 Hagenauer, Julian [VerfasserIn]   i
 Farajzadeh, Manuchehr [VerfasserIn]   i
 Helbich, Marco [VerfasserIn]   i
Titel:Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change
Titelzusatz:a case study
Verf.angabe:Hossein Shafizadeh-Moghadam, Julian Hagenauer, Manuchehr Farajzadeh and Marco Helbich
E-Jahr:2015
Jahr:11 March 2015
Umfang:18 S.
Fussnoten:Gesehen am 17.06.2020
Titel Quelle:Enthalten in: International journal of geographical information science
Ort Quelle:London : Taylor & Francis, 1987
Jahr Quelle:2015
Band/Heft Quelle:29(2015), 4, Seite 606-623
ISSN Quelle:1365-8824
Abstract:The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling.
DOI:doi:10.1080/13658816.2014.993989
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1080/13658816.2014.993989
 DOI: https://doi.org/10.1080/13658816.2014.993989
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:GIS
 multi-layer perceptron network
 radial basis function network
 spatial accuracy assessment
 urban change
K10plus-PPN:1700723537
Verknüpfungen:→ Zeitschrift

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