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Assessing and mapping landslide susceptibility using different machine learning methods / Osman Orhan in Geocarto international, vol 37 n° 10 ([01/06/2022])  

Public [article]  inGeocarto international > vol 37 n° 10 [01/06/2022] . - pp 2795 - 2820 Titre : Assessing and mapping landslide susceptibility using different machine learning methods Type de document : Article/Communication Auteurs : Osman Orhan, Auteur ; Suleyman Sefa Bilgilioglu, Auteur ; Zehra Kaya, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2795 - 2820 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] carte thématique [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] effondrement de terrain [Termes IGN] lithologie [Termes IGN] pente [Termes IGN] régression logistique [Termes IGN] réseau neuronal artificiel [Termes IGN] séparateur à vaste marge [Termes IGN] Turquie Résumé : (auteur) The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide incidents occur in Turkey. Firstly, a landslide inventory was produced by identifying a total of 252 landslides. Secondly, a total of 11 landslide conditioning factors were considered for the landslide susceptibility mapping. Subsequently, the five machine learning techniques were constructed with the help of the training dataset for the landslide susceptibility maps. Finally, the receiver operating characteristic (ROC), sensitivity, specificity, F-measure, accuracy and kappa index were applied to compare and validate the performance of the five machine learning techniques. Numéro de notice : A2022-594 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1837258 Date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1837258 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101298 [article]