Centre de documentation
scientifique

Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models / Kai Cui in Geocarto international, vol 32 n° 9 (September 2017)  

Public [article]  inGeocarto international > vol 32 n° 9 (September 2017) . - pp 935 - 955 Titre : Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models Type de document : Article/Communication Auteurs : Kai Cui, Auteur ; Dong Lu, Auteur ; Wei Li, Auteur Année de publication : 2017 Article en page(s) : pp 935 - 955 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aléa [Termes IGN] analyse comparative [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] effondrement de terrain [Termes IGN] inventaire [Termes IGN] pondération [Termes IGN] risque naturel [Termes IGN] système d'information géographique [Termes IGN] théorie de Dempster-Shafer [Termes IGN] vulnérabilité Résumé : (Auteur) The main aim of this study was to produce landslide susceptibility maps using statistical index (SI), certainty factors (CF), weights of evidence (WoE) and evidential belief function (EBF) models for the Long County, China. Firstly, a landslide inventory map, including a total of 171 landslides, was compiled on the basis of earlier reports, interpretation of aerial photographs and supported by extensive field surveys. Thereafter, all landslides were randomly separated into two data sets: 70% landslides (120 points) were selected for establishing the model and the remaining landslides (51 points) were used for validation purposes. Eleven landslide conditioning factors, such as slope aspect, slope angle, plan curvature, profile curvature, altitude, distance to faults, distance to roads, distance to rivers, lithology, NDVI and land use, were considered for landslide susceptibility mapping in this study. Then, the SI, CF, WoE and EBF models were used to produce the landslide susceptibility maps for the study area. Finally, the four models were validated using area under the curve (AUC) method. According to the validation results, the EBF model (AUC = 78.93%) has a higher prediction accuracy than the SI model (AUC = 77.72%), the WoE model (AUC = 77.62%) and the CF model (AUC = 77.72%). Similarly, the validation results also indicate that the EBF model has the highest training accuracy of 80.25%, followed by SI (79.80%), WoE (79.71%) and CF (79.67%) models. Numéro de notice : A2017-457 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.0/10106049.2016.1195886 Date de publication en ligne : 16/06/2016 En ligne : http://dx.doi.org108/10.0/10106049.2016.1195886 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86382 [article]