Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques

Geoderma

Volumn 305, Issue , 2017, Pages 314-327

  Chen, Wei ^a   Pourghasemi, Hamid Reza ^b   Kornejady, Aiding ^c   Zhang, Ning ^a  

^a XI'AN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b SHIRAZ UNIVERSITY   (Iran)

^c GORGAN UNIVERSITY OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES   (Iran)

Author keywords

ANN; Ensemble models; Maximum entropy; Spatial modeling; SVM

Indexed keywords

ARTIFICIAL INTELLIGENCE; EDUCATION; LANDSLIDES; LEARNING ALGORITHMS; LEARNING SYSTEMS; LITHOLOGY; MAXIMUM ENTROPY METHODS; NEURAL NETWORKS; STATISTICAL TESTS;

ENSEMBLE MODELS; ENVIRONMENTAL CONDITIONS; LANDSLIDE SUSCEPTIBILITY; MACHINE LEARNING TECHNIQUES; NORMALIZED DIFFERENCE VEGETATION INDEX; RECEIVER OPERATING CHARACTERISTIC CURVES; SPATIAL MODELING; TOPOGRAPHIC WETNESS INDEX;

SUPPORT VECTOR MACHINES;

ARTIFICIAL NEURAL NETWORK; LAND COVER; LANDSLIDE; LIMESTONE; MAXIMUM ENTROPY ANALYSIS; NDVI; SLOPE ANGLE; SPATIAL ANALYSIS; SUPPORT VECTOR MACHINE;

CHINA; SICHUAN; WANYUAN;

EID: 85021392934     PISSN: 00167061     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.geoderma.2017.06.020     Document Type: Article

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