A nonlinear data-driven model for synthetic generation of annual streamflows

Hydrological Processes

Volumn 22, Issue 12, 2008, Pages 1831-1845

  Sudheer, K P ^a   Srinivasan, K ^a   Neelakantan, T R ^b   Srinivas, V V ^c  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

^b SASTRA UNIVERSITY   (India)

^c INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

Data driven models; Moving block bootstrap; Non linear hybrid; Radial basis function neural network; Stream flow generation

Indexed keywords

AUTOMATIC TRAIN CONTROL; BLENDING; DROUGHT; EIGENVALUES AND EIGENFUNCTIONS; FEEDFORWARD NEURAL NETWORKS; FOOD PRESERVATION; HYBRID SENSORS; IMAGE CLASSIFICATION; MATHEMATICAL MODELS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; REAL TIME SYSTEMS; STREAM FLOW;

ARTIFICIAL NEURAL NETWORK (ANN) MODELS; ARTIFICIAL NEURAL NETWORK (ANNS); CASE STUDIES; DATA DRIVEN (DD); DATA-DRIVEN MODELING; GEOGRAPHIC REGIONS; HYBRID MODEL (HM); LINEAR DEPENDENCES; MARGINAL DISTRIBUTIONS; NEURAL NETWORK (NN) MODELS; NON LINEAR MODELING; NON-LINEAR; NON-LINEARITIES; NON-PARAMETRIC; NONLINEAR DATA; RADIAL-BASIS FUNCTION (RBF); RE SAMPLING; STORAGE CHARACTERISTICS; STREAM FLOW RECORDS; SYNTHETIC GENERATION;

BACKPROPAGATION;

ARTIFICIAL NEURAL NETWORK; BOOTSTRAPPING; HYDROLOGICAL MODELING; PARAMETERIZATION; RADIAL FLOW; RIVER FLOW; SAMPLING; SMOOTHING; STREAMFLOW;

AFRICA; NORTH AMERICA; UNITED STATES; USSR;

EID: 47249103851     PISSN: 08856087     EISSN: 10991085     Source Type: Journal    
DOI: 10.1002/hyp.6764     Document Type: Article

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