A Remote sensing-based approach for debris-flow susceptibility assessment using artificial neural networks and logistic regression modeling

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 7, Issue 12, 2014, Pages 4818-4835

  Elkadiri, Racha ^a   Sultan, Mohamed ^a   Youssef, Ahmed M ^b   Elbayoumi, Tamer ^c   Chase, Ronald ^a   Bulkhi, Ali B ^b   Al Katheeri, Mohamed M ^b  

^a WESTERN MICHIGAN UNIVERSITY   (United States)

^b SAUDI GEOLOGICAL SURVEY   (Saudi Arabia)

^c OKLAHOMA STATE UNIVERSITY   (United States)

Author keywords

Artificial neural networks (ANN); Data mining; Data scarce field regions; Debris flows; Geographical information system (GIS); Logistic regression (LR); Remote sensing

Indexed keywords

COST EFFECTIVENESS; DATA MINING; DEBRIS; GEOGRAPHIC INFORMATION SYSTEMS; GEOGRAPHICAL REGIONS; INFORMATION SYSTEMS; NEURAL NETWORKS; REGRESSION ANALYSIS;

ARTIFICIAL NEURAL NETWORK MODELS; DATA-SCARCE FIELD REGIONS; DEBRIS FLOWS; DEBRIS-FLOW SUSCEPTIBILITIES; LOGISTIC REGRESSION MODELING; LOGISTIC REGRESSION MODELS; LOGISTIC REGRESSIONS; NORMALIZED DIFFERENCE VEGETATION INDEX;

REMOTE SENSING;

ARTIFICIAL NEURAL NETWORK; DATA MINING; DEBRIS FLOW; GIS; NDVI; REGRESSION ANALYSIS; REMOTE SENSING;

RED SEA HILLS; RED SEA [(STT) SUDAN]; SAUDI ARABIA; SUDAN;

EID: 85027934968     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2337273     Document Type: Article

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