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Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: a case study from the Sikkim Himalayas in India / L.P. Sharma in Geocarto international, vol 29 n° 1 - 2 (February - April 2014)  

Public [article]  inGeocarto international > vol 29 n° 1 - 2 (February - April 2014) . - pp 128 - 146 Titre : Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: a case study from the Sikkim Himalayas in India Type de document : Article/Communication Auteurs : L.P. Sharma, Auteur ; Nilanchal Patel, Auteur ; Mrinal K. Ghose, Auteur ; P. Debnath, Auteur Année de publication : 2014 Article en page(s) : pp 128 - 146 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] ArcGIS [Termes IGN] cartographie des risques [Termes IGN] effondrement de terrain [Termes IGN] fréquence [Termes IGN] Himalaya [Termes IGN] Inde [Termes IGN] modélisation spatiale [Termes IGN] partition d'image [Termes IGN] risque majeur [Termes IGN] risque naturel [Termes IGN] système d'information géographique [Termes IGN] vulnérabilité [Termes IGN] zone à risque Résumé : (Auteur) The Likelihood Ratio (LR) Model has been applied as an improvement upon the Frequency Ratio (FR) that computes the ratio of the percentage of the landslide pixels to the percentage of the non-landslide pixels instead of the total number of pixels used in the denominator as in case of the FR. The comparative assessment of the two techniques is made through spatial modelling of GIS vector data using the ArcGIS software. Two different Landslide Information Values were computed for each polygon element of the study area employing the two FR techniques that categorized the study area into five classes of vulnerability using natural breaks (Jenks) technique. Subsequently, vulnerability zonation maps were prepared showing the different levels of landslide vulnerability. The LR technique yielded significantly higher vulnerability assessment accuracy (77%) as compared to the standard FR (71%). Numéro de notice : A2014-237 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.748830 Date de publication en ligne : 06/02/2013 En ligne : https://doi.org/10.1080/10106049.2012.748830 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33140 [article]

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Landslide vulnerability assessment and zonation through ranking of causative parameters based on landslide density-derived statistical indicators / L. Sharma in Geocarto international, vol 26 n° 6 (October 2011)  

Public [article]  inGeocarto international > vol 26 n° 6 (October 2011) . - pp 491 - 504 Titre : Landslide vulnerability assessment and zonation through ranking of causative parameters based on landslide density-derived statistical indicators Type de document : Article/Communication Auteurs : L. Sharma, Auteur ; N. Patel, Auteur ; M. Ghose, Auteur ; P. Debnath, Auteur Année de publication : 2011 Article en page(s) : pp 491 - 504 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] données statistiques [Termes IGN] effondrement de terrain [Termes IGN] indice de risque [Termes IGN] risque naturel [Termes IGN] système d'information géographique [Termes IGN] vulnérabilité [Termes IGN] zone à risque Résumé : (Auteur) The research presented in this article is based on a new technique governed by three different statistical indicators determined for each causative parameter, viz. highest density, average density and coefficient of variation of landslides. Each of these indicators was assigned a rank value between 1 and 14 depending upon its variation among the 14 causative parameters. The aggregate of the three types of rank values estimate the total ranking value (TRV) for each causative parameter. The study area is divided into 78,256 spatial units and for each such spatial unit, the influence of the different causative parameters is determined as the product of the experts' weight of the associated sub-category and the TRV of the causative parameter that categorizes the study area into various zones. The efficacy of the proposed technique is demonstrated by the occurrence of significantly high prediction accuracy of 84%. Numéro de notice : A2011-403 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.598951 Date de publication en ligne : 01/08/2011 En ligne : https://doi.org/10.1080/10106049.2011.598951 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78076 [article]

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059-2011061	RAB	Revue	Centre de documentation	En réserve L003	Disponible