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Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran / Hossein Shafizadeh-Moghadam in Computers, Environment and Urban Systems, vol 87 (May 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101595 Titre : Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Masoud Minaei, Auteur ; Robert Gilmore Pontius Jr, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101595 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] croissance urbaine [Termes IGN] extrapolation [Termes IGN] image Landsat [Termes IGN] modèle de simulation [Termes IGN] occupation du sol [Termes IGN] Téhéran [Termes IGN] utilisation du sol Résumé : (auteur) This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the most important drivers during 2008–2018. We used the Total Operating Characteristic to evaluate the transition index maps. Validation during 2008–2018 showed that FFS-RF produced a transition index map that had predictive power no better than an allocation of urban growth near existing urban. Simulation to 2060 extrapolated that Tehran, Karaj, and their adjacent cities will interconnect spatially to form a gigantic city-region. Numéro de notice : A2021-274 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101595 Date de publication en ligne : 16/02/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101595 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97357 [article]

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study / Hossein Shafizadeh-Moghadam in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 606 - 623 Titre : Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Julian Hagenauer, Auteur ; Manuchehr Farajzadeh, Auteur ; Marco Helbich, Auteur Année de publication : 2015 Article en page(s) : pp 606 - 623 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] Bombay [Termes IGN] croissance urbaine [Termes IGN] fonction de base radiale [Termes IGN] milieu urbain [Termes IGN] modèle de simulation [Termes IGN] Perceptron multicouche [Termes IGN] performance [Termes IGN] test de performance [Termes IGN] urbanisation Résumé : (Auteur) 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. Numéro de notice : A2015-589 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.993989 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.993989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77875 [article]