Landslide susceptibility mapping along the upper Jinsha River, south-western China: a comparison of hydrological and curvature watershed methods for slope unit classification
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Title
Landslide susceptibility mapping along the upper Jinsha River, south-western China: a comparison of hydrological and curvature watershed methods for slope unit classification
Authors
Keywords
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Journal
Bulletin of Engineering Geology and the Environment
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-05-29
DOI
10.1007/s10064-020-01849-0
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- (2018) Jiewei Zhan et al. Environmental Earth Sciences
- Improving landslide susceptibility mapping using morphometric features in the Mawat area, Kurdistan Region, NE Iraq: Comparison of different statistical models
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- Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques
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- Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India
- (2017) Deepak Kumar et al. GEOMORPHOLOGY
- Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques
- (2017) Wei Chen et al. GEOMORPHOLOGY
- The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements
- (2017) S. Steger et al. Landslides
- Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China
- (2017) Fei Wang et al. ISPRS International Journal of Geo-Information
- Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area
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- Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China
- (2016) Chen Cao et al. Water
- Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines
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- An Application of SVM-Based Classification in Landslide Stability
- (2015) Tingyao Jiang et al. INTELLIGENT AUTOMATION AND SOFT COMPUTING
- Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree
- (2015) Dieu Tien Bui et al. Landslides
- GIS deterministic model-based 3D large-scale artificial slope stability analysis along a highway using a new slope unit division method
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- An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: The Candir catchment area (western Antalya, Turkey)
- (2013) B. Taner San International Journal of Applied Earth Observation and Geoinformation
- Landslide inventory maps: New tools for an old problem
- (2012) Fausto Guzzetti et al. EARTH-SCIENCE REVIEWS
- Mean-curvature watersheds: A simple method for segmentation of a digital elevation model into terrain units
- (2011) Bård Romstad et al. GEOMORPHOLOGY
- Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides
- (2010) Xiaojun Yang et al. International Journal of Applied Earth Observation and Geoinformation
- Optimal landslide susceptibility zonation based on multiple forecasts
- (2009) Mauro Rossi et al. GEOMORPHOLOGY
- Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China
- (2008) X. Yao et al. GEOMORPHOLOGY
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