Comparison of two data-driven approaches for daily river flow forecasting

MODSIM 2011 - 19th International Congress on Modelling and Simulation - Sustaining Our Future: Understanding and Living with Uncertainty

Volumn , Issue , 2011, Pages 1077-1083

  Fernando, Achela K ^a   Shamseldin, Asaad Y ^b   Abrahart, Robert J ^c  

^a UNITEC INSTITUTE OF TECHNOLOGY   (New Zealand)

^b UNIVERSITY OF AUCKLAND   (New Zealand)

^c UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Artificial Neural Network; Gene Expression Programming; Genetic Algorithms; Rainfall runoff model; River flow forecasting

Indexed keywords

ANTECEDENT RAINFALL; BLACK BOXES; BLUE NILE; COMPUTING SOFTWARE; DAILY RAINFALL; DATA-DRIVEN; DATA-DRIVEN APPROACH; EXTERNAL INPUT; FLOW DATA; FORECASTING MODELS; GENE EXPRESSION PROGRAMMING; INDEPENDENT VARIABLES; MODEL DEVELOPMENT; RAINFALL RUNOFF; RAINFALL-RUNOFF MODELLING; RAINFALL-RUNOFF MODELS; RIVER DISCHARGE; RIVER FLOW; RIVER FLOW FORECASTING; SYMBOLIC REGRESSION;

CATCHMENTS; GENETIC ALGORITHMS; NEURAL NETWORKS; RAIN; RUNOFF; SOFT COMPUTING; STREAM FLOW; UNCERTAINTY ANALYSIS;

FORECASTING;

EID: 84858804117     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

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3. Babovic, V., and Keijzer, M. (2002). "Rainfall Runoff Modelling Based on Genetic Programming." Nordic Hydrology, 33(5), 1-346.
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7. Ferreira, C. (2001). "Gene Expression Programming: A New Adaptive Algorithm for Solving Problems." Complex Systems, 13(2), 87-129
8. Ferreira, C. (2006). Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, 2nd Edition, Springer-Verlag, Germany.
9. Shamseldin, A. Y. (1997). " Application of neural network technique to rainfall-runoff modelling." Journal of Hydrology, 199(3), 272-294.
10. Shamseldin, A. Y., O'Connor, K. M., and Nasr, A. E. (2007). "A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall-runoff models." Hydrological Sciences, 52(5), 896-916.