A scalable approach to fusing spatiotemporal data to estimate streamflow via a bayesian network

IEEE Transactions on Geoscience and Remote Sensing

Volumn 48, Issue 10, 2010, Pages 3720-3732

  Nagarajan, Karthik ^a   Krekeler, Carolyn ^a   Slatton, K Clint ^a   Graham, Wendy D ^b  

^a University of Florida   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

Bayesian network (BN); data fusion; feature selection; hydrology; information theory; spatiotemporal data fusion

Indexed keywords

BAYESIAN INFERENCE; BAYESIAN NETWORKS (BNS); CONDITIONAL ENTROPY; DATA FUSION METHODS; DATA TYPE; DENSE NETWORK; FEATURE SELECTION; GROUND WATER LEVEL; HYDROLOGIC MODELS; IN-SITU; LAND COVER; NUMBER OF DATUM; OBSERVED DATA; PARAMETER VALUES; PHYSICALLY BASED; POSTERIOR PROBABILITY; PREDICTION ACCURACY; QUALITY OF PREDICTIONS; REMOTE SENSING IMAGERY; SCALABLE APPROACH; SPATIO-TEMPORAL APPROACH; SPATIO-TEMPORAL DATA; SPATIOTEMPORAL DOMAIN; STREAM NETWORKS; STREAMFLOW MEASUREMENTS; STUDY AREAS; SUPERLINEAR;

COMPUTATIONAL COMPLEXITY; DATA FUSION; DISTRIBUTED PARAMETER NETWORKS; ENTROPY; FEATURE EXTRACTION; FORECASTING; GROUNDWATER; INFERENCE ENGINES; INFORMATION FUSION; INFORMATION THEORY; INTELLIGENT NETWORKS; LANDFORMS; MATHEMATICAL MODELS; REMOTE SENSING; SPEECH RECOGNITION; STREAM FLOW; UNCERTAINTY ANALYSIS; WATER MANAGEMENT; WATERSHEDS;

BAYESIAN NETWORKS;

ACCURACY ASSESSMENT; BAYESIAN ANALYSIS; DATA PROCESSING; FLOOD CONTROL; FLOODING; HYDROLOGICAL MODELING; INSTRUMENTATION; POLLUTANT TRANSPORT; PREDICTION; PROBABILITY; REMOTE SENSING; SEDIMENT TRANSPORT; SPATIOTEMPORAL ANALYSIS; STREAMFLOW; WATER MANAGEMENT; WATERSHED;

EID: 77957006253     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2010.2049115     Document Type: Article

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