Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions

Prediction of monthly discharge volume is important for reservoir management and evaluation of drinking-water supplies. Also, it is very essential in arid and semi-arid regions due to the lack of observed data. This study compared four artificial neural network (ANN) algorithms to predict the monthly discharge volume from Idenak Watershed in Kohkiloye Boier Ahmad Province in southwestern Iran. These algorithms, including resilient backpropagation (ANN_RP), scaled conjugate gradient (ANN_SCG), variable learning rate (ANN_GDX), and Levenberg–Marquardt (ANN_LM), were applied to monthly discharge volume data. The transfer function employed was the tangent sigmoid, and input vectors were constructed in different ways during the algorithm development. The algorithms were trained and tested using a 36-year data record (432 monthly values) selected randomly. Comparison of the algorithms showed that ANN_SCG performed better than the other algorithms, where the values of R2 and root mean square errors during validation were 0.78 and 63 million cubic meters. Furthermore, the input vector consisting of precipitation [P(t)], antecedent precipitation [P(t − 1)], and antecedent monthly discharge volume with one time lag [V(t− 1)] was superior to the other input vectors for monthly discharge volume prediction. Generally, the proposed models are capable for prediction of monthly discharge volume in arid and semi-arid regions.

[1]  Hossein Tabari,et al.  Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran , 2010, Neural Computing and Applications.

[2]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .

[3]  P. Gelder,et al.  Forecasting daily streamflow using hybrid ANN models , 2006 .

[4]  A. Tokar,et al.  Rainfall-Runoff Modeling Using Artificial Neural Networks , 1999 .

[5]  J. J. Moré,et al.  Levenberg--Marquardt algorithm: implementation and theory , 1977 .

[6]  Momcilo Markus,et al.  PRECIPITATION-RUNOFF MODELING USING ARTIFICIAL NEURAL NETWORKS AND CONCEPTUAL MODELS , 2000 .

[7]  Hossein Tabari,et al.  Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression , 2010, Irrigation Science.

[8]  Hossein Tabari,et al.  Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region , 2011 .

[9]  Davar Khalili,et al.  Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions , 2010 .

[10]  K. P. Sudheer,et al.  A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .

[11]  Martin Fodslette Meiller.  A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .

[12]  K. P. Sudheer,et al.  Rainfall‐runoff modelling using artificial neural networks: comparison of network types , 2005 .

[13]  Mehdi Rezaeian Zadeh,et al.  Maximum Daily Discharge Prediction using Multi Layer Perceptron Network , 2009 .

[14]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[15]  Hossein Tabari,et al.  Numerical Model and Computational Intelligence Approaches for Estimating Flow through Rockfill Dam , 2012 .

[16]  Ozgur Kisi.  Neural network and wavelet conjunction model for modelling monthly level fluctuations in Turkey. , 2009 .

[17]  D. H. Pilgrim,et al.  Problems of rainfall-runoff modelling in arid and semiarid regions , 1988 .

[18]  A. Shamseldin.  Application of a neural network technique to rainfall-runoff modelling , 1997 .

[19]  B. L. Kalman,et al.  Why tanh: choosing a sigmoidal function , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[20]  Hydrological characteristics of arid zones , 2007 .

[21]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[22]  Ozgur Kisi,et al.  River Flow Modeling Using Artificial Neural Networks , 2004 .

[23]  Holger R. Maier,et al.  The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study , 1998 .

[24]  Jy-Shing Wu,et al.  Artificial Neural Networks for Forecasting Watershed Runoff and Stream Flows , 2005 .

[25]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[26]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[27]  Ozgur Kisi,et al.  Stream flow forecasting using neuro‐wavelet technique , 2008 .

[28]  H. Tabari,et al.  Predicting Spatial Distribution of Snow Water Equivalent Using Multivariate Non-linear Regression and Computational Intelligence Methods , 2011 .

[29]  Ozgur Kisi,et al.  Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data , 2005 .

[30]  E. D. Martonne.  L'indice d'aridité , 1926 .