Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

Computers and Geosciences

Volumn 37, Issue 4, 2011, Pages 410-425

  Melchiorre, C ^a   Castellanos Abella, E A ^b   van Westen, C J ^c   Matteucci, M ^d  

^a GEOLOGICAL SURVEY OF NORWAY   (Norway)

^b Institute of Geology and Palaeontology (IGP)   (Cuba)

^c UNIVERSITY OF TWENTE   (Netherlands)

^d POLITECNICO DI MILANO   (Italy)

Author keywords

Artificial neural networks; Landslide susceptibility mapping; Model evaluation

Indexed keywords

10-FOLD CROSS-VALIDATION; ARTIFICIAL NEURAL NETWORK; BLACK-BOX MODEL; DATA SETS; DRAINAGE DENSITY; GENERALIZATION CAPABILITY; HIGH ROBUSTNESS; LANDSLIDE SUSCEPTIBILITY; LANDSLIDE SUSCEPTIBILITY MAPPING; MODEL EVALUATION; NON-LINEAR; PREDICTION CAPABILITY; RAINFALL INTENSITY; ROBUSTNESS ANALYSIS; SLOPE ANGLES; TEST SETS; TRAINING PROCEDURES; TRAINING SETS;

FORECASTING; LANDSLIDES; SENSITIVITY ANALYSIS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; DRAINAGE; GEOLOGICAL MAPPING; GEOMORPHOLOGY; LAND USE; LANDSLIDE; NUMERICAL MODEL; PRECIPITATION INTENSITY; PREDICTION; RELIEF; SLOPE ANGLE;

CUBA; GUANTANAMO;

EID: 79952622006     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2010.10.004     Document Type: Article

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