Abstract
This paper discusses the use of artificial neural network (ANN) models for predicting daily flows from Khosrow Shirin watershed located in the northwest part of Fars province in Iran. A Multi-Layer Perceptron (MLP) neural network was developed using five input vectors leading to five ANN models: MLP1, MLP2, MLP3, MLP4, and MLP5. Two activation functions were used and they were logistic sigmoid and tangent sigmoid. The MLP_Levenberg–Marquardt (LM) algorithm was used for the training of ANN models. A 5-year data record, selected randomly, was used for ANN training and testing. The predicted outflow showed that the tangent sigmoid activation function performed better than did the logistic sigmoid activation function. The values of R 2 and RMSE for MLP4 with the tangent sigmoid activation function for the validation period were equal to 0.89 and 1.7 m3/s, respectively. Appropriate input vectors for MLPs were determined by correlation analysis. It was found that antecedent precipitation and discharge with 1 day time lag as an input vector best predicted daily flows. Also, comparison of MLPs showed that an increase in input data was not always useful.
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Ahmed JA, Sarma AK (2009) Artificial neural network model for synthetic streamflow generation. Water Resour Manag 21:1015–1029
Amin S, Soltani Tudeshki AR (2003) Using artificial neural networks in prediction runoff and sediment. In: Proceeding of water-saving agricultural and sustainable use of water and land resources, vol 2. Yangling, Shaanxi, China, pp 821–832
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5(2):115–123
Beven KJ (2001) Rainfall–runoff modelling: the primer. Wiley, Chichester
Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall–runoff modeling. Hydrol Sci J 43(1):47–66
Gopakumar R, Takara K, James EJ (2007) Hydrologic data exploration and river flow forecasting of a humid tropical river basin using artificial neural networks. Water Resour Manag 21:1915–1940
Govindaraju RS, Rao AR (2000) Artificial neural networks in hydrology. Kluwer Academic, The Netherlands
Haykin S (1999) Neural networks: a comprehensive foundation. Pearson Education, Singapore
Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539
Kumar APS, Sudheer KP, Jain SK, Agarwal PK (2005) Rainfall–runoff modeling using artificial neural networks: comparison of network types. Hydrol Process 19:1277–1291
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15:101–123
Modarres R (2009) Multi-criteria validation of artificial neural network rainfall–runoff modeling. Hydrol Earth Syst Sci 13:411–421
Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32(1):88–97
More JJ (1977) The Levenberg–Marquardt algorithm: implementation and theory, numerical analysis. In: Watson GA (ed) Lecture notes in mathematics 630. Springer, New York, pp 105–116
Nayebi M, Khalili D, Amin S, Zand-Parsa Sh (2006) Daily stream flow prediction capability of artificial neural networks as influenced by minimum air temperature data. Biosyst Eng 95(4):557–567
Rezaeian Zadeh M, Abghari H, van de Giesen N, Nikian A, Niknia N (2009) Maximum daily discharge prediction using multi layer perceptron network. Geophys Res Abstr 11:3652–3652, EGU General Assembly 2009
Rumelhart DE, McClelland JL, PDP Research Group (1986) Parallel distributed processing foundations, vol 1. MIT, Cambridge
Shirsath PB, Singh AK (2009) A comparative study of daily pan evaporation estimation using ANN, regression and climate based models. Water Resour Manag. doi:10.1007/s11269-009-9514-2
Soroosh M, Gity F, Sherafat AR, Farahani Kh, Razaghi M (2005) A neural network model for determination of the breakdown voltage for separate absorption and multiplication region avalanche photodiode (SAM-APD). Second IEEE Conf. on Wireless and Optical Communication Networks, UAE, pp 173–177
Sudheer KP, Gosain AK, Ramasastri KS (2002) A data driven algorithm for constructing artificial neural network rainfall–runoff models. Hydrol Process 16(6):1325–1330
Tokar AS, Johnson A (1999) Rainfall–runoff modeling using artificial neural networks. J Hydrol Eng ASCE 4(3):232–239
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Rezaeian Zadeh, M., Amin, S., Khalili, D. et al. Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions. Water Resour Manage 24, 2673–2688 (2010). https://doi.org/10.1007/s11269-009-9573-4
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DOI: https://doi.org/10.1007/s11269-009-9573-4