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A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping / Wei Chen in Geocarto international, vol 32 n° 4 (April 2017)  

Public [article]  inGeocarto international > vol 32 n° 4 (April 2017) . - pp 367 - 385 Titre : A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Wei Chen, Auteur ; Hamid Reza Pourghasemi, Auteur ; Zhou Zhao, Auteur Année de publication : 2017 Article en page(s) : pp 367 - 385 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aléa [Termes IGN] analyse comparative [Termes IGN] ArcGIS [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] classification de Dempster-Shafer [Termes IGN] classification par réseau neuronal [Termes IGN] effondrement de terrain [Termes IGN] régression logistique [Termes IGN] risque naturel [Termes IGN] vulnérabilité Résumé : (Auteur) The main aim of present study is to compare three GIS-based models, namely Dempster–Shafer (DS), logistic regression (LR) and artificial neural network (ANN) models for landslide susceptibility mapping in the Shangzhou District of Shangluo City, Shaanxi Province, China. At First, landslide locations were identified by aerial photographs and supported by field surveys, and a total of 145 landslide locations were mapped in the study area. Subsequently, the landslide inventory was randomly divided into two parts (70/30) using Hawths Tools in ArcGIS 10.0 for training and validation purposes, respectively. In the present study, 14 landslide conditioning factors such as altitude, slope angle, slope aspect, topographic wetness index, sediment transport index, stream power index, plan curvature, profile curvature, lithology, rainfall, distance to rivers, distance to roads, distance to faults and normalized different vegetation index were used to detect the most susceptible areas. In the next step, landslide susceptible areas were mapped using the DS, LR and ANN models based on landslide conditioning factors. Finally, the accuracies of the landslide susceptibility maps produced from the three models were verified using the area under the curve (AUC). The validation results showed that the landslide susceptibility map generated by the ANN model has the highest training accuracy (73.19%), followed by the LR model (71.37%), and the DS model (66.42%). Similarly, the AUC plot for prediction accuracy presents that ANN model has the highest accuracy (69.62%), followed by the LR model (68.94%), and the DS model (61.39%). According to the validation results of the AUC curves, the map produced by these models exhibits the satisfactory properties. Numéro de notice : A2017-271 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1140824 Date de publication en ligne : 22/03/2016 En ligne : http://doi.org/10.1080/10106049.2016.1140824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85297 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2017041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Integrating cellular automata and Markov techniques to generate urban development potential surface : a study on Kolkata agglomeration / Biswajit Mondal in Geocarto international, vol 32 n° 4 (April 2017)  

Public [article]  inGeocarto international > vol 32 n° 4 (April 2017) . - pp 401 - 419 Titre : Integrating cellular automata and Markov techniques to generate urban development potential surface : a study on Kolkata agglomeration Type de document : Article/Communication Auteurs : Biswajit Mondal, Auteur ; Dipendra Nath Das, Auteur ; Basudeb Bhatta, Auteur Année de publication : 2017 Article en page(s) : pp 401 - 419 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] allocation [Termes IGN] automate cellulaire [Termes IGN] Calcutta [Termes IGN] chaîne de Markov [Termes IGN] croissance urbaine [Termes IGN] Inde [Termes IGN] modèle de simulation [Termes IGN] prospective [Termes IGN] répartition géographique [Termes IGN] surveillance de l'urbanisation [Termes IGN] urbanisation [Termes IGN] utilisation du sol Résumé : (Résumé) Uncontrolled, yet fragmented peripheral urban expansion has emerged as a menace to urban development. To cope with this rapid urban expansion process, identification of the forces responsible for this rapid urban expansion is a pre-requisite, especially when its threats to habitability are taken into consideration. This study tries to evaluate fragmented uncontrolled urban expansion faced by Kolkata using cellular automata-Markov chain. Urban growth patterns, land use/land cover transformations and spatial allocation correspondence with planning strategy is the main theme of this study. Depending upon the driving forces, the study result indicates a bi-directional urban development potential surface, which might be a result of the biased planning initiative along with middle-class residential demand. This simulation result provides evidence for the planning authority to evaluate the effectiveness of spatial allocation and urban expansion trends and provide flexibility to modify the current allocation scenario. Numéro de notice : A2017-272 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1155656 Date de publication en ligne : 23/03/2016 En ligne : http://doi.org/10.1080/10106049.2016.1155656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85299 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2017041	RAB	Revue	Centre de documentation	En réserve L003	Disponible