2013
DOI: 10.3126/bdg.v15i0.7419
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Landslide susceptibility analysis using decision tree method, Phidim, Eastern Nepal

Abstract: The decision tree is one of the new methods used for the determination of landslide susceptibility in the study area. The Phidim area is selected for the application of this method. The total surface area is 168.07 sq. km, and is located at the eastern part of Nepal. There are total of 10 different data bases used for this study which are; geological formation, elevation, slope, curvature, aspect, stream power index, topographic wetness index, distance from drainage, lineaments, and slope length, and are consi… Show more

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Cited by 11 publications
(5 citation statements)
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“…When compared to the results achieved in similar studies, the AUC value obtained for Porto Alegre can be considered high (e.g. Suh et al 2011;Akgün 2012;Poudyal 2012;Guillard and Zêzere, 2012). It is believed that the high AUC value obtained is mainly due to the support of local experts in all steps of the study, accompanied by a careful analysis of the set of criteria.…”
Section: Resultsmentioning
confidence: 99%
“…When compared to the results achieved in similar studies, the AUC value obtained for Porto Alegre can be considered high (e.g. Suh et al 2011;Akgün 2012;Poudyal 2012;Guillard and Zêzere, 2012). It is believed that the high AUC value obtained is mainly due to the support of local experts in all steps of the study, accompanied by a careful analysis of the set of criteria.…”
Section: Resultsmentioning
confidence: 99%
“…Paper Accepted: 06 February 2016 There are ample of studies (Dangol 2002, Ghimire 2010, Kayastha et al 2012, 2013, Poudyal 2012) on landslide hazard assessment in Nepal Himalaya. Most of the researches were focused on central and eastern Nepal.…”
Section: Paper Received: 08 May 2015mentioning
confidence: 99%
“…In portions having high and very high susceptible areas, the role of drainage is most important in determining the susceptibility (Poudyal 2012). Nearer the stream higher the weight values and farther the stream, the weight values decreases.…”
Section: Fig 5: Landslide Inventory Of Research Sitementioning
confidence: 99%
“…Worldwide, researchers employed methods such as the AHP [14,15], F-AHP [15], fuzzy logic [16], frequency ratio [17], analytical network process [18], FROC [19], and Mora-Vahrson-Mora (MVM) [20,21] for the mapping of landslide-susceptible zones. Researchers also applied artificial intelligence (AI)/machine learning (ML) models such as support vector machine (SVM) [22][23][24], Naïve Bayes (NB) [25][26][27], decision tree [28][29][30], K-nearest neighbor [31,32], random forest (RF) [33,34], adaptive neuro-fuzzy inference system (ANFIS) [35], convolutional neural network (CNN) [36][37][38], artificial neural network [39,40], logistic regression [41][42][43], support vector regression [44,45], recurrent neural network [36,37], Adaptive Boosting [46], extreme gradient boosting [35], Random Subspace (RSS) [47], Reduced Error Pruning Tree (REPTree) [48], etc., for landslide susceptibility modeling. The mixed ensemble models of ML techniques such as multi-layer perceptron [49], ANFIS [50], genetic algorithm [51], RSS…”
Section: Introductionmentioning
confidence: 99%