Event-based sediment yield modeling using artificial neural network

Water Resources Management

Volumn 22, Issue 4, 2008, Pages 423-441

  Rai, Raveendra K ^a   Mathur, B S ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

ANN; Automated Bayesian Regularization; Event based; Runoff; Sediment yield; Sedimentograph; Small watersheds

Indexed keywords

AUTOCORRELATION; AUTOMATA THEORY; BACKPROPAGATION; BAYESIAN NETWORKS; FEEDFORWARD NEURAL NETWORKS; FLOW RATE; GRADIENT METHODS; MEAN SQUARE ERROR; RUNOFF; TRANSFER FUNCTIONS; WATERSHEDS;

CORRELATION COEFFICIENT; HYDROGRAPH; NASH EFFICIENCY; ROOT MEAN SQUARE ERROR; SEDIMENT YIELD; SEDIMENTOGRAPH;

SEDIMENT TRANSPORT;

AUTOCORRELATION; AUTOMATA THEORY; BACKPROPAGATION; BAYESIAN NETWORKS; FEEDFORWARD NEURAL NETWORKS; FLOW RATE; GRADIENT METHODS; MEAN SQUARE ERROR; RUNOFF; SEDIMENT TRANSPORT; TRANSFER FUNCTIONS; WATERSHEDS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; AUTOCORRELATION; BACK PROPAGATION; BAYESIAN ANALYSIS; HYDROGRAPH; MODELING; RUNOFF; SEDIMENT YIELD; SEDIMENTOLOGY; TRANSFER FUNCTION; TRAVEL TIME; WATERSHED;

EID: 40549084354     PISSN: 09204741     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11269-007-9170-3     Document Type: Article

References (35)

1. Agarwal A, Singh RD (2004) Runoff modeling through back propagation artificial neural network with variable rainfall-runoff data. Water Resour Manag 18:285-300
2. Agarwal A, Singh RD, Mishra SK, Bhunya PK (2005) ANN-based sediment yield models for Vamsadhara river basin (India). Water SA 31(1):95-100
3. ASCE Task committee on application of artificial neural networks in hydrology (2000a) Artificial neural networks in hydrology. I. Preliminary concepts. J Hydrol Eng ASCE 5(2):115-123
4. ASCE Task committee on application of artificial neural networks in hydrology (2000b) Artificial neural networks in hydrology. II. Hydrologic applications. J Hydrol Eng ASCE 5(2):124-137
5. Birikundavyi S, Labib R, Trung HT, Rousselle J (2002) Performance of neural networks in daily streamflow forecasting. J Hydrol Eng 7(5):392-398
6. Campolo M, Andreussi P, Soldati A (1999) River flood forecasting with a neural network model. Water Resour Res 35(4):1191-1197
7. Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multilayer perceptrons. Adv Water Resour 27:185-195
8. Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall-runoff modelling. Hydrol Sci J 43(1):47-66
9. Fernando DAK, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng ASCE 3(3):203-209
10. Foresee FD, Hagan M (1997) Gauss-Newton approximation to Bayesian learning. IEEE Proc of Conf on ANN 4:1930-1935
11. Haykin S (1999) Neural Network - a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey
12. Hsu K-L, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517-2530
13. Jain A, Indurthy PKV (2003) Comparative analysis of event based rainfall-runoff modeling techniques - deterministic, statistical and artificial neural networks. J Hydrol Eng 8(2):93-98
14. Keskin ME, Terzi Ö (2006) Artificial neural network models of daily pan evaporation. J Hydrol Eng 11(1):65-70
15. Kim T-W, Valdes JB (2003) Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. J Hydrol Eng 8(6):319-328
16. Kirpich ZP (1940) Time of concentration of small agricultural watersheds. Civ Eng 10(6):362
17. Lange N (1998) Advantage of unit hydrograph derivation by neural networks. In: Babovie V, Larsen CL (eds) Hydroinformatics, Proc. 3rd Int. Conf. on Hydroinformatics. Copenhagen, Denmark, 2:783-789, A.A. Balkema, Rotterdam, Netherlands
18. Minns AW, Hall MJ (1996) Artificial neural networks as rainfall-runoff models. Hydrol Sci J 41(3):399-417
19. Moradkhani H, Hsu K-L, Gupta HV, Sorooshian S (2004) Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. J Hydrol 295:246-262
20. Nagy HM, Watanabe K, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. J Hydraul Eng 128(6):588-595
21. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. J Hydrol 10:282-290
22. Olsson J, Uvo CB, Jinno K, Kawamura A, Nishiyama K, Koreeda N, Nakashima T, Morita O (2004) Neural networks for rainfall forecasting by atmospheric downscaling. J Hydrol Eng 9(1):1-12
23. Pradhan MK, Ramu TS (2004) On-line monitoring of temperature in power transformers using optimal linear combination of ANNs. IEEE Int Symp on Electrical Insulation, Indianapolis, Indiana, USA, 19-22 September 2004: 70-73
24. Raghuwanshi NS, Singh R, Reddy LS (2006) Runoff and sediment yield modeling using artificial neural networks: Upper siwane river, India. J Hydrol Eng 11(1):71-79
25. Rajurkar MP, Kothyari UC, Chaube UC (2002) Artificial neural networks for daily rainfall-runoff modeling. Hydrol Sci J 47(6):865-877
26. Rajurkar MP, Kothyari UC, Chaube UC (2004) Modeling of the daily rainfall-runoff relationship with artificial neural networks. J Hydrol 285:96-113
27. Sajikumar S, Thandaveswara BS (1999) A non-linear rainfall-runoff model using an artificial neural network. J Hydrol 216:32-55
28. Salas JD, Deulleur JW, Yevjevich V, Lane WL (1980) Applied modelling of hydrologic time series. Water Resources Publications, Littleton, CO
29. Smith J, Eli RN (1995) Neural network models of rainfall-runoff process. J Water Resour Plan Manage ASCE 121(6):499-507
30. Sudheer KP, Jain SK (2003) Radial basis function neural network for modeling rating curves. J Hydrol Eng 8(3):161-164
31. Tayfur G (2002) Artificial neural networks for sheet sediment transport. Hydrol Sci J 47(6):879-892
32. Thirumalaiah K, Deo MC (2000) Hydrological forecasting using neural networks. J Hydrol Eng 5(2):180-189
33. Tokar AS, Markus M (2000) Precipitation-runoff modeling using artificial neural networks and conceptual models. J Hydrol Eng 5(2):156-161
34. Vemuri VR (1992) Artifical neural networks: Concepts and control application. IEEE Computer Society Press, Los Alamitos, CA
35. Zhang B, Govindaraju RS (2000) Prediction of watershed runoff using Bayesian concepts and modular neural networks. Water Resour Res 36(3):753-762