Abstract
In the present study, a back propagation feedforward artificial neural network (ANN) model was developed for the computation of event-based temporal variation of sediment yield from the watersheds. The training of the network was performed by using the gradient descent algorithm with automated Bayesian regularization, and different ANN structures were tried with different input patterns. The model was developed from the storm event data (i.e. rainfall intensity, runoff and sediment flow) registered over the two small watersheds and the responses were computed in terms of runoff hydrographs and sedimentographs. Selection of input variables was made by using the autocorrelation and cross-correlation analysis of the data as well as by using the concept of travel time of the watershed. Finally, the best fit ANN model with suitable combination of input variables was selected using the statistical criteria such as root mean square error (RMSE), correlation coefficient (CC) and Nash efficiency (CE), and used for the computation of runoff hydrographs and sedimentographs. Further, the relative performance of the ANN model was also evaluated by comparing the results obtained from the linear transfer function model. The error criteria viz. Nash efficiency (CE), error in peak sediment flow rate (EPS), error in time to peak (ETP) and error in total sediment yield (ESY) for the storm events were estimated for the performance evaluation of the models. Based on these criteria, ANN based model results better agreement than the linear transfer function model for the computation of runoff hydrographs and sedimentographs for both the watersheds.
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Abbreviations
- t i :
-
Target output at node i
- a i :
-
Network output at node i
- N:
-
Number of observation
- \( \overline{X} _{{k + 1}} \) :
-
Weight factor at iteration (k + 1)
- \( \overline{g} \) :
-
\( = \nabla f{\left( {\overline{X} _{{_{k} }} } \right)} = \) error gradient vector
- Y norm :
-
Normalized dimensionless variable
- Y i :
-
Observed value of variable
- Y min :
-
Minimum value of variable
- Y max :
-
Maximum value of variable
- O (i) :
-
Output at ith hidden node
- O n :
-
Net output at ith hidden node
- Q t :
-
Direct runoff at time t
- Q(t − r):
-
Direct runoff at lag-r
- S t :
-
Sediment flow at time t
- S O :
-
Observed sediment flow
- S C :
-
Computed sediment flow
- \( \overline{S} _{{\text{O}}} \) :
-
Mean of observed sediment flow
- E D :
-
Sum of square error
- E W :
-
Sum of square network weights
- F :
-
Objective function
- λ :
-
Parameter of objective function
- η :
-
Parameter of objective function
- S(t − p):
-
Sediment flow at lag-p
- R t :
-
Rainfall intensity at time t
- R(t − q):
-
Rainfall intensity at lag-q
- p, q, r:
-
integer
- n :
-
Chosen step size
- k :
-
Lag
- CE:
-
Nash efficiency
- EPS:
-
Error in peak sediment flow rate
- ETP:
-
Error in time to peak
- ESY:
-
Error in sediment yield
- RMSE:
-
Root mean square error
- CC:
-
Correlation coefficient
References
Agarwal A, Singh RD (2004) Runoff modeling through back propagation artificial neural network with variable rainfall–runoff data. Water Resour Manag 18:285–300
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
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
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
Birikundavyi S, Labib R, Trung HT, Rousselle J (2002) Performance of neural networks in daily streamflow forecasting. J Hydrol Eng 7(5):392–398
Campolo M, Andreussi P, Soldati A (1999) River flood forecasting with a neural network model. Water Resour Res 35(4):1191–1197
Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multilayer perceptrons. Adv Water Resour 27:185–195
Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall–runoff modelling. Hydrol Sci J 43(1):47–66
Fernando DAK, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng ASCE 3(3):203–209
Foresee FD, Hagan M (1997) Gauss–Newton approximation to Bayesian learning. IEEE Proc of Conf on ANN 4:1930–1935
Haykin S (1999) Neural Network—a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey
Hsu K-L, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall–runoff process. Water Resour Res 31(10):2517–2530
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
Keskin ME, Terzi Ö (2006) Artificial neural network models of daily pan evaporation. J Hydrol Eng 11(1):65–70
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
Kirpich ZP (1940) Time of concentration of small agricultural watersheds. Civ Eng 10(6):362
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
Minns AW, Hall MJ (1996) Artificial neural networks as rainfall–runoff models. Hydrol Sci J 41(3):399–417
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
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
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. J Hydrol 10:282–290
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
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
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
Rajurkar MP, Kothyari UC, Chaube UC (2002) Artificial neural networks for daily rainfall–runoff modeling. Hydrol Sci J 47(6):865–877
Rajurkar MP, Kothyari UC, Chaube UC (2004) Modeling of the daily rainfall–runoff relationship with artificial neural networks. J Hydrol 285:96–113
Sajikumar S, Thandaveswara BS (1999) A non-linear rainfall–runoff model using an artificial neural network. J Hydrol 216:32–55
Salas JD, Deulleur JW, Yevjevich V, Lane WL (1980) Applied modelling of hydrologic time series. Water Resources Publications, Littleton, CO
Smith J, Eli RN (1995) Neural network models of rainfall–runoff process. J Water Resour Plan Manage ASCE 121(6):499–507
Sudheer KP, Jain SK (2003) Radial basis function neural network for modeling rating curves. J Hydrol Eng 8(3):161–164
Tayfur G (2002) Artificial neural networks for sheet sediment transport. Hydrol Sci J 47(6):879–892
Thirumalaiah K, Deo MC (2000) Hydrological forecasting using neural networks. J Hydrol Eng 5(2):180–189
Tokar AS, Markus M (2000) Precipitation-runoff modeling using artificial neural networks and conceptual models. J Hydrol Eng 5(2):156–161
Vemuri VR (1992) Artifical neural networks: concepts and control application. IEEE Computer Society Press, Los Alamitos, CA
Zhang B, Govindaraju RS (2000) Prediction of watershed runoff using Bayesian concepts and modular neural networks. Water Resour Res 36(3):753–762
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Rai, R.K., Mathur, B.S. Event-based Sediment Yield Modeling using Artificial Neural Network. Water Resour Manage 22, 423–441 (2008). https://doi.org/10.1007/s11269-007-9170-3
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DOI: https://doi.org/10.1007/s11269-007-9170-3