Rainfall-runoff modelling using genetic programming

MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings

Volumn , Issue , 2005, Pages 1841-1847

  Jayawardena, A W ^a   Muttil, N ^b   Fernando, T M K G ^c  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^c UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Data driven models; Evolutionary algorithms; Genetic programming; Rainfall runoff modelling

Indexed keywords

APPROPRIATE MODELS; BUILDING BLOCKES; COMPLEX MODEL; DATA SETS; DATA-DRIVEN; DATA-DRIVEN MODEL; EVACUATION PROCEDURES; EVOLUTIONARY SEARCH; FUNCTION SETS; FUNCTIONAL FORMS; GOODNESS-OF-FIT MEASURE; HONG-KONG; INPUT AND OUTPUTS; INPUT VARIABLES; LEARNING METHODS; MODEL ACCURACY; MODEL COEFFICIENT; NETWORK STRUCTURES; OPTIMAL STRUCTURES; PEAK DISCHARGE; PHYSICAL CHARACTERISTICS; PHYSICS-BASED; PREDICTION MODEL; RAINFALL-RUNOFF MODELLING; RAINFALL-RUNOFF PROCESS; REGRESSION METHOD; RIVER FLOW; RUNOFF PREDICTION; TIME INTERVAL; TIME VARYING; TIME-CONSUMING TASKS; DATA DRIVEN MODELLING; DATA DRIVEN TECHNIQUE; RAINFALL - RUNOFF MODELLING; SIGNIFICANT VARIABLES; TIME OF CONCENTRATIONS;

CATCHMENTS; DECISION MAKING; EVOLUTIONARY ALGORITHMS; FORECASTING; GENETIC ALGORITHMS; GENETIC PROGRAMMING; LEARNING ALGORITHMS; MATHEMATICAL MODELS; METADATA; NEURAL NETWORKS; RAIN; STRUCTURAL OPTIMIZATION; WATER RESOURCES; COMPLEX NETWORKS; LEARNING SYSTEMS; PREDICTIVE ANALYTICS; REGRESSION ANALYSIS;

RUNOFF;

EID: 68949124303     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

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