A comparison of artificial neural networks for prediction of suspended sediment discharge in river-a case study in Malaysia

World Academy of Science, Engineering and Technology

Volumn 81, Issue , 2011, Pages 372-376

  Mustafa, M R ^a   Isa, M H ^a   Rezaur, R B ^b  

^a DEPARTMENT OF CHEMICAL ENGINEERING   (Malaysia)

^b GOLDER ASSOCIATES LTD   (Canada)

Author keywords

ANN; Discharge; Modeling; Prediction; Suspended sediment

Indexed keywords

ANN; COEFFICIENT OF DETERMINATION; MALAYSIA; MEAN ABSOLUTE ERROR; MULTI-LAYER FEED FORWARD; NETWORK MODELS; NONLINEAR BEHAVIOR; OBSERVED DATA; PERFORMANCE EVALUATION; PREDICTIVE PERFORMANCE; RBF NETWORK MODEL; ROOT MEAN SQUARE ERRORS; STATISTICAL PARAMETERS; TIME-SERIES DATA; TRAINING AND TESTING; WATER DISCHARGES;

ANOXIC SEDIMENTS; DISCHARGE (FLUID MECHANICS); FORECASTING; MEAN SQUARE ERROR; MODELS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; RIVERS; SEDIMENTOLOGY; TIME SERIES; WATER RESOURCES;

SUSPENDED SEDIMENTS;

EID: 80053453552     PISSN: 2010376X     EISSN: 20103778     Source Type: Journal    
DOI: None     Document Type: Article

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