Deterministic insight into ANN model performance for storm runoff simulation

Water Resources Management

Volumn 22, Issue 1, 2008, Pages 67-82

  Lee, Kwan Tun ^a   Hung, Wei Chiao ^a   Meng, Chung Chieh ^a  

^a NATIONAL TAIWAN OCEAN UNIVERSITY   (Taiwan)

Author keywords

ANN model; Deterministic insight; Rainfall runoff simulation; Training data set

Indexed keywords

COMPUTER SIMULATION; DISCHARGE (FLUID MECHANICS); NEURAL NETWORKS; RAIN; STORMS; WATERSHEDS;

RAINFALL-RUNOFF PROCESS; STORM RUNOFF SIMULATION;

RUNOFF;

COMPUTER SIMULATION; DISCHARGE (FLUID MECHANICS); NEURAL NETWORKS; RAIN; RUNOFF; STORMS; WATERSHEDS;

ARTIFICIAL NEURAL NETWORK; DATA SET; RAINFALL-RUNOFF MODELING; RUNOFF; SIMULATION; SPATIAL VARIATION; STORMWATER; TEMPORAL VARIATION; VECTOR;

EID: 37549051650     PISSN: 09204741     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11269-006-9144-x     Document Type: Article

References (39)

1. Amorocho J (1967) The nonlinear prediction problem in the study of runoff cycle. Water Resour Res 3(3):861-880
2. Anmala J, Zhang B, Govindaraju RS (2000) Comparison of ANNs and empirical approaches for predicting watershed runoff. J Water Resour Plan Manage, ASCE 126(3):156-166
3. ASCE Task Committee (2000a) Artificial neural networks in hydrology. I: Preliminary concepts. J Hydrol Eng ASCE 5(2):115-123
4. ASCE Task Committee (2000b) Artificial neural networks in hydrology. II: Hydrologic applications. J Hydrol Eng ASCE 5(2):124-137
5. Caudill M (1987) Neural networks primer. Part I. AI expert:46-52
6. Chiu CL, Huang JT (1970) Nonlinear time-varying model of rainfall-runoff relation. Water Resour Res 6(1):1277-1286
7. Chow VT (1959) Open-channel hydraulics. McGraw-Hill, New York
8. Chow VT (1964) Handbook of applied hydrology. McGraw-Hill, New York
9. Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall-runoff modeling. Hydrol Sci J 43(1):47-66
10. Ding JY (1974) Variable unit hydrograph. J Hydrol 22:53-69
11. Dooge JCI (1959) A general theory of the unit hydrograph. J Geophys Res 64(1):241-256
12. Engman ET (1986) Roughness coefficients for routing surface runoff. J Irrig Drain Eng ASCE 112(1):39-53
13. French MN, Krajewski WF, Cuykendall RR (1992) Rainfall forecasting in space and time using a neural network. J Hydrol 137:1-31
14. Haykin S (1994) Neural Networks: A comprehensive foundation. MacMillan, New York
15. Hopfield JJ (1982) Neural networks and physical system with emergent collective computational abilities. Proc Natl Acad Sci 79:2554-2558
16. Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517-2530
17. Hydrologic Engineering Center (2000) Hydrologic modeling system HEC-HMS, Technical reference manual, U. S. Army Corps of Engineers.
18. Jain A, Indurthy SKVP (2003) Comparative analysis of event-based rainfall-runoff modeling techniques - deterministic, statistical, and artificial neural networks. J Hydrol Eng ASCE 8(2):93-98
19. Kumar DN, Raju KS, Sathish T (2004) River flow forecasting using recurrent neural networks. Water Resour Manag 18:143-161
20. Lee KT (1998) Generating design hydrographs by DEM assisted geomorphic runoff simulation: A case study. J Am Water Resour Assoc 34(2):375-384
21. Lee KT, Yen BC (1997) Geomorphology and kinematic-wave based hydrograph deviation. J Hydraul Eng ASCE 123(1):73-80
22. Lee KT, Chang C-H, Yang M-S, Yu W-S (2001) Reservoir attenuation of floods from ungauged basins. Hydrol Sci J 46(3):349-362
23. Lee KT, Chung Y-R, Lau C-C, Meng C-C, Chiang S (2006a) A windows-based inquiry system for design discharge based on geomorphic runoff modeling. Comput Geosci 32(2):203-211
24. Lee KT, Ho Y-H, Chyan Y-J (2006b) Bridge blockage and overbank flow simulations using HEC-RAS in the Keelung River during the 2001 Nari typhoon. J. Hydraul Eng, ASCE 132(3):319-323
25. Muleta MK, Nicklow JW (2004) Joint application of artificial neural networks and evolutionary algorithms to watershed management. Water Resour Manag 18:459-482
26. Minshall NE (1960) Predicting storm runoff on small experimental watersheds. J Hydraul Eng ASCE HY8:17-38
27. Nash JE (1957) The form of instantaneous unit hydrograph. Int'l Assoc Sci Hydrol, Pub 45(3):114-121
28. Rao SVN, Thandaveswara BS, Bhallamudi M, Srinivasulu V (2003) Optimal groundwater management in deltaic regions using simulated annealing and neural networks. Water Resour Manag 17:409-428
29. Rodriguez-Iturbe I, Valdes JB (1979) The geomorphologic structure of hydrologic response. Water Resour Res 15(6):1409-1420
30. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. Parallel Distributed Processing. MIT, Cambridge, MA, pp 318-362
31. Sherman LK (1932) Streamflow from rainfall by unit-graph method. Eng News-Rec 108(7):501-505
32. Singh VP (1996) Kinematic wave modeling in water resources - surface water hydrology. Wiley, USA.
33. Tayfur G (2002) Artificial neural networks for sheet sediment transport. Hydrol Sci J 47(6):879-892
34. Thirumalaiah K, Deo MC (2000) Hydrological forecasting using neural networks. J Hydrol Eng ASCE 5(2):180-189
35. Tokar AS, Johnson PA (1999) Rainfall-runoff modeling using artificial neural networks. J Hydrol Eng ASCE 4(3):232-239
36. Weltz MA, Arslan AB, Lane LJ (1992) Hydraulic roughness coefficients for native rangelands. J Irrig Drain Eng ASCE 118(5):776-790
37. Yapo PO, Gupta VK, Sorooshian S (1996) Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data. J Hydrol 181:23-48
38. Yen BC, Lee KT (1997) Unit hydrograph derivation for ungaged watersheds by stream order laws. J Hydrol Eng ASCE 2(1):1-9
39. Zealand CM, Burn DH, Simonovic SP (1999) Short term streamflow forecasting using artificial neural networks. J Hydrol 214:32-48