Simulation and analysis of runoff from a partly glaciated meso-scale catchment area in Patagonia using an artificial neural network

Hydrological Processes

Volumn 23, Issue 7, 2009, Pages 1019-1030

  Sauter, Tobias ^a   Schneider, Christoph ^a   Kilian, Rolf ^b   Moritz, Michael ^b  

^a RWTH AACHEN UNIVERSITY   (Germany)

^b Department of Geology FB VI ^*  (Germany)

Author keywords

Artificial neural network; Global sensifivity analysis; Patagonia; Rainfall runoff modelling; South America; Stream flow forecasting

Indexed keywords

ATMOSPHERIC RADIATION; BACKPROPAGATION; CATCHMENTS; CONVERGENCE OF NUMERICAL METHODS; NEURAL NETWORKS; SEA LEVEL; SEAWATER; SENSITIVITY ANALYSIS; WATER LEVELS; WEATHER FORECASTING; WEATHER INFORMATION SERVICES;

ARTIFICIAL NEURAL NETWORK; GLOBAL SENSIFIVITY ANALYSIS; PATAGONIA; RAINFALL-RUNOFF MODELLING; SOUTH AMERICA; STREAM-FLOW FORECASTING;

RUNOFF;

AIR TEMPERATURE; ARTIFICIAL NEURAL NETWORK; CONFERENCE PROCEEDING; MULTIPLE REGRESSION; PRECIPITATION (CLIMATOLOGY); RAINFALL-RUNOFF MODELING; RUNOFF; SENSITIVITY ANALYSIS; STREAMFLOW; WEATHER STATION; WIND VELOCITY;

ANDES; PATAGONIA; SOUTH AMERICA;

EID: 62249181485     PISSN: 08856087     EISSN: 10991085     Source Type: Journal    
DOI: 10.1002/hyp.7210     Document Type: Conference Paper

References (37)

1. Akaike H. 1974. A new look at the statistical model identification. IEEE Transactions on Automotive Control 19: 716-723.
2. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000a. Artificial neural networks in hydrology, I: preliminary concepts. Journal of Hydrologic Engineering ASCE 5(2): 115-123.
3. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000b. Artificial neural networks in hydrology, II: hydrological applications. Journal of Hydrologic Engineering ASCE 5(2): 124-137.
4. Beven KJ. 2001. Rainfall-Runoff Modelling. John Wiley and Sons: Chichester, UK; 372.
5. Dawson CW, Wilby R. 2001. Hydrological modelling using artificial neural networks. Progress in Physical Geography 25: 80-108.
6. Demuth H, Beale M, Hagan M. 2005. Neural Network Toolbox for use with Matlab, Version 4. The Mathworks: Natik.
7. Fischbach T, Behrmann J, Kilian R, Hegner E, Hohner M. 2000. Geotectonic and geochemical implications on the tectonic development of the southernmost Andes, Kurzfassung zur 17. LAK in Stuttgart, 49.
8. Freeman JA, Skapura DM. 1991. Neural Networks: Algorithms, Applications, and Programming Techniques. Addison-Wesley Publishing Company: New York.
9. Hommat T, Saltelli A. 1996. Important measures in global sensitivity analysis of nonlinear models. Reliability Engineering & System Safety 52(1): 1-17.
10. Hsu K, Gupta HV, Gao X, Sorooshian S. 1995. Artificial neural network modelling of the rainfall-runoff process. Water Resources Research 31(10): 2517-2530.
11. Jain A, Sudheer KP, Srinivasulu S. 2004. Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrological Processes 18: 571-581.
12. JRC. 2006. Website of SIMLAB software and download at Joint Research Center, Ispra, http://webfarm.jrc.cec.eu.int/uasa/primer/index.asp.
13. Kasabov NK. 1998. Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. The MIT Press: Cambridge.
14. Kecman V. 2001. Learning and Soft Computing. The MIT Press: Cambridge.
15. Maier HR, Dandy GC. 1998. The effect of internal parameters and geometry on the performance of backpropagation neural networks: an empirical study. Enviromental Modelling and Software 13: 193-209.
16. Maier HR, Dandy DC. 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Enviromental Modelling and Software 15: 101-123.
17. Minns AW, Hall MJ. 2004. Rainfall-runoff modelling. In Neural Networks for Hydrological Modelling, Abrahart RJ, Kneale PE (eds). Balkema: Leiden, Chap. 9.
18. Minns AW, Hall MJ. 2005. Artificial neural network concepts in hydrology. In Encyclopedia of Hydrological Sciences, Anderson MG, McDonnell JJ (eds). John Wiley and Sons: Chichester.
19. Möller M, Schneider C, Kilian R. 2007. Glacier change and climate forcing at Gran Campo Nevado, southernmost Patagonia. Annals of Glaciology 46: 136-144.
20. Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptual models part I - A discussion of principles. Journal of Hydrology 10(3): 282-290.
21. Poddig T, Sidorovitch S. 2001. Künstliche Neuronale Netze: Überblick, Einsatzmöglichkeiten und Anwendungsprobleme ( Artificial Neural Networks: Overview, Application and Problems). In Handbuch Data Mining im Marketing, Hippner H, Kuestner U, Meyer M, Wilde K (eds). Vieweg Verlagsgesellschaft: Braunschweig; 363-402.
22. Ratto M, Saltelli A. 2001. Gluewin Users Manual. In Model Assessment in Integrated Procedures for Environmental Impact Evaluation: Software Prototypes, IMPACT Project (SCA, DG-IST, 1999-11313), Deliverable 18.
23. Rissanen J. 1978. Modeling by short data description. Automation 14: 465-471.
24. Saltelli A, Tarantola S, Campolongo F. 2000. Sensitivity Analysis as an ingredient of modeling. Statistical Science 15(4): 877-895.
25. Saltelli A, Tarantola S, Campolongo F, Ratto M. 2004. Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley and Sons: Chichester.
26. Schneider C, Glaser M, Kilian R, Santana A, Butorovic N, Casassa G. 2003. Weather observations across the Southern Andes at 53°. Physical Geography 24: 97-119.
27. Schneider C, Kilian R, Glaser M. 2007a. Energy balance in the ablation zone during the summer season at the Gran Campo Nevado Ice Cap in the Southern Andes. Global and Planetary Change 59(1): 175-188.
28. Schneider C, Schnirch M, Acuña C, Casassa G, Kilian R. 2007b. Glacier inventory of the Gran Campo Nevado Ice Cap in the Southern Andes and glacier changes observed during recent decades. Global and Planetary Change 59(1): 87-100.
29. Shamseldin AY. 1997. Application of a neural network technique to rainfall-runoff modelling. Journal of Hydrology 199: 272-294.
30. Sobol IM. 2001. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation 55: 271-280.
31. Solomatine DP. 2005. Data-driven modeling and computational intelligence methods in hydrology. In Encyclopedia of Hydrological Sciences, Anderson MG, McDonnell JJ (eds). John Wiley and Sons: Chichester.
32. Sudheer KP, Gosain AK, Ramasastri KS. 2002. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrological Processes 16: 1325-1330.
33. Sudheer KP, Jain A. 2004. Explaining the internal behaviour of artificial neural network river flow models. Hydrological Processes 18: 833-844.
34. Tsoukalas LH, Uhrig RE, Zadeh LA. 1997. Fuzzy and Neural Approaches in Engineering. John Wiley and Sons: Chichester.
35. Uvo CB, Toelle U, Berndtsson R. 2000. Forecasting discharge in amazonia using artificial neural network. International Journal of Climatology 20: 1495-1507.
36. Wilby RL, Abrahart RJ, Dawson CW. 2003. Detecting of conceptual model rainfall-runoff processes inside an artificial neural network. Hydrological Sciences Journal 48(2): 163-181.
37. Zell A. 2003. Simulation Neuronaler Netze (Simulation with Neuronal Networks). Wissenschaftsverlag: Oldenbourg.