International Journal of Geosciences

International Journal of Geosciences

ISSN Print: 2156-8359
ISSN Online: 2156-8367
www.scirp.net/journal/ijg
E-mail: [email protected]
"Multi-Resolution Landslide Susceptibility Analysis Using a DEM and Random Forest"
written by Uttam Paudel, Takashi Oguchi, Yuichi Hayakawa,
published by International Journal of Geosciences, Vol.7 No.5, 2016
has been cited by the following article(s):
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[4] EFFECT OF THE CURVATURE PARAMETER AND İTS CLASSİFİCATİON ON LANDSLİDES
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[5] Influencing Physical Characteristics of Landslides in Kuala Lumpur, Malaysia.
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[9] Landslide Hazard Assessment in Highway Areas of Guangxi Using Remote Sensing Data and a Pre-Trained XGBoost Model
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[10] Image-data-driven deep learning for slope stability analysis
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[11] Evaluation of The Landslide Processes In Cayambe, Pichincha, Ecuador
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[12] PREPARING COASTAL EROSION VULNERABILITY INDEX IN ODISHA APPLYING GEOSPATIAL AND MACHINE LEARNING TECHNIQUES
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[13] Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility
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[14] Stability prediction of a natural and man-made slope using various machine learning algorithms
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[15] Landslide susceptibility assessment using AHP model and multi resolution DEMs along a highway in Manipur, India
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[16] A combined method for preparation of landslide susceptibility map in Izmir (Türkiye)
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[17] Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas
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[18] Prediction model of the slope angle of rocky slope stability based on random forest algorithm
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[19] Improved tree-based machine learning algorithms combining with bagging strategy for landslide susceptibility modeling
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[20] Impact study for landslide contributing factors using a multi-criterion approach for landslide susceptibility
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[21] A unified network of information considering superimposed landslide factors sequence and pixel spatial neighbourhood for landslide susceptibility mapping
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[22] Evaluation of different DEMs for gully erosion susceptibility mapping using in-situ field measurement and validation
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[23] Random Forests for Landslide Prediction in Tsengwen River Watershed, Central Taiwan
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[24] Significance of the Spatial Resolution of DEM in Regional Slope Stability Analysis Enguri Dam, Republic of Georgia
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[25] Random Forests for Landslide Prediction in Tsengwen River Watershed, Central Taiwan. Remote Sens. 2021, 13, 199
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[26] Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM
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[27] DEM Resolutions for Landslide Susceptibility Modeling
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[28] Оценка склоновых процессов кантона Каямбе провинции Пичинча (Эквадор)
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[29] Performance evaluation of gis-based artificial intelligence approaches for landslide susceptibility modeling and spatial patterns analysis
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[30] Stability prediction of Himalayan residual soil slope using artificial neural network
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[31] On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification
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[32] Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping
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[33] The performance of landslide susceptibility models critically depends on the quality of digital elevations models
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[34] A landslide susceptibility map based on spatial scale segmentation: A case study at Zigui-Badong in the Three Gorges Reservoir Area, China
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[35] Gis-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques
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[36] The (f) utility to account for pre-failure topography in data-driven landslide susceptibility modelling
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[37] Optimizing collapsed pipes mapping: Effects of DEM spatial resolution
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[38] Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms
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[39] An Ensemble Model for Landslide Susceptibility Mapping in a Forested Area
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[40] Assessing landslide characteristics in a changing climate in northern Taiwan
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[41] Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study
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[42] AN EVALUATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING REMOTE SENSING DATA AND MACHINE LEARNING ALGORITHMS IN IRAN.
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[43] Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data
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[44] Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques
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[45] Scaling land-surface variables for landslide detection
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[46] An evaluation of landslide susceptibility mapping using remote sensing data and machine learning algorithms in Iran
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[47] Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution
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[48] Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees
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[49] Sensitivity of land-surface variables to scale in identifying landslide scarps
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[50] Evaluating the susceptibility of landslide landforms in Japan using slope stability analysis: a case study of the 2016 Kumamoto earthquake
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[51] Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms
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[52] Evaluating the susceptibility of landslide landforms in Japan using slope stability analysis: A case study for the Kumamoto earthquake
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[53] Analyzing rainfall-induced mass movements in Taiwan using the soil water index
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