Centre de documentation
scientifique

GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region, Indian Himalaya / Satish Kumar in Geocarto international, vol 32 n° 11 (November 2017)  

Public [article]  inGeocarto international > vol 32 n° 11 (November 2017) . - pp 1254 - 1267 Titre : GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region, Indian Himalaya Type de document : Article/Communication Auteurs : Satish Kumar, Auteur ; Pankaj Kumar Srivastava, Auteur ; Snehmani, Auteur Année de publication : 2017 Article en page(s) : pp 1254 - 1267 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aléa [Termes IGN] analyse multicritère [Termes IGN] avalanche [Termes IGN] cartographie des risques [Termes IGN] Himalaya [Termes IGN] image Landsat-8 [Termes IGN] image Terra-ASTER [Termes IGN] Inde [Termes IGN] outil d'aide à la décision [Termes IGN] plan de prévention des risques [Termes IGN] processus de hiérarchisation analytique [Termes IGN] risque naturel [Termes IGN] vulnérabilité Résumé : (Auteur) Avalanches are behind the majority of fatalities and heavy damage to property in snow-covered mountainous terrain like Himalaya. Recognizing avalanche susceptible areas and publication of avalanche susceptibility maps assist decision-makers and planners to execute suitable measures to reduce the avalanche risk. The present study is an attempt to prepare an avalanche susceptibility map of the Nubra valley region using multi-criteria decision analysis–analytical hierarchy process model in GIS environment. The most prominent avalanche occurrence factors used in this model are slope, aspect, curvature, elevation, terrain roughness and ground cover. ASTER GDEM V2 and Landsat 8 satellite imagery were used to generate considered factors. For validation of the results, prediction rate/accuracy is calculated using the avalanche inventory map of documented avalanche locations. To calculate the prediction accuracy, area under the ROC curve (ROC-AUC) method has been used. The prediction accuracy of the validation results using ROC-AUC shows 91%. Numéro de notice : A2017-671 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1206626 Date de publication en ligne : 13/07/2016 En ligne : https://doi.org/10.1080/10106049.2016.1206626 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87157 [article]