Comparison of Rainfall-Runoff Relationship Modeling using Different Methods in a Forested Watershed

The daily rainfall-runoff relationship in an experimental watershed was modeled using a statistical method and an artificial neural network method. The estimations were examined and a performance evaluation was done. It was seen that the ANN method, FFBP (Feed Forward Back Propagation), provided closer flow estimations reproducing the shape of the observed hydrograph more realistic. The superiority of FFBP was reflected in the performance evaluation criteria. The extreme flows, i.e., high and low flows, were relatively better approximated by FFBP indicating its promise as a useful tool for hydrologic studies such as flood modeling. The Rational Method was also used, as a conventional tool, to predict the maximum discharge for selected return periods. It was found to be realistic for the forested watershed under consideration when the C coefficient was taken as 0.20 for the 10-year period.

[1]  K. Thirumalaiah,et al.  River Stage Forecasting Using Artificial Neural Networks , 1998 .

[2]  Richard Labib,et al.  Performance of Neural Networks in Daily Streamflow Forecasting , 2002 .

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

[4]  Russell C. Eberhart,et al.  Neural network PC tools: a practical guide , 1990 .

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

[6]  R. K. Rai,et al.  Event-based Sediment Yield Modeling using Artificial Neural Network , 2008 .

[7]  S. Takeuchi,et al.  Runoff Analysis for a Small Watershed of Tono Area Japan by Back Propagation Artificial Neural Network with Seasonal Data , 2008 .

[8]  Indrajeet Chaubey,et al.  Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed , 2008 .

[9]  Hikmet Kerem Cigizoglu,et al.  Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data , 2007, Environ. Model. Softw..

[10]  H. K. Cigizoglu,et al.  Incorporation of ARMA models into flow forecasting by artificial neural networks , 2003 .

[11]  Hikmet Kerem Cigizoglu,et al.  Generalized regression neural network in monthly flow forecasting , 2005 .

[12]  A. W. Jayawardena,et al.  Runoff Forecasting Using RBF Networks with OLS Algorithm , 1998 .

[13]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

[14]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[15]  J. Eheart,et al.  Neural network-based screening for groundwater reclamation under uncertainty , 1993 .

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

[17]  Vijay P. Singh,et al.  Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree , 2010 .

[18]  J. Randolph.  Environmental Land Use Planning and Management , 2003 .

[19]  Hikmet Kerem Cigizoglu,et al.  Estimation, forecasting and extrapolation of river flows by artificial neural networks , 2003 .

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

[21]  Ozgur Kisi,et al.  Methods to improve the neural network performance in suspended sediment estimation , 2006 .

[22]  S. Özhan,et al.  Sediment and nutrient discharge through streamflow from two experimental watersheds in mature oak-beech forest ecosystems near Istanbul, Turkey , 1986 .

[23]  Turgay Partal,et al.  Estimation and forecasting of daily suspended sediment data using wavelet–neural networks , 2008 .

[24]  Avinash Agarwal,et al.  Runoff Modelling Through Back Propagation Artificial Neural Network With Variable Rainfall-Runoff Data , 2004 .

[25]  N. Seçkin,et al.  Comparison of Artificial Neural Network Methods with L-moments for Estimating Flood Flow at Ungauged Sites: the Case of East Mediterranean River Basin, Turkey , 2013, Water Resources Management.

[26]  H. K. Cigizoglu,et al.  ESTIMATION AND FORECASTING OF DAILY SUSPENDED SEDIMENT DATA BY MULTI-LAYER PERCEPTRONS , 2004 .

[27]  K. Thirumalaiah,et al.  Hydrological Forecasting Using Neural Networks , 2000 .

[28]  Y. Serengil,et al.  Cover and management factors for the Universal Soil-Loss Equation for forest ecosystems in the Marmara region, Turkey , 2005 .

[29]  Vladimir U. Smakhtin,et al.  Hydrological Modeling of Large river Basins: How Much is Enough? , 2014, Water Resources Management.

[30]  H. Altun,et al.  Treatment of multi-dimensional data to enhance neural network estimators in regression problems , 2006 .

[31]  Hikmet Kerem Cigizoglu,et al.  Application of Generalized Regression Neural Networks to Intermittent Flow Forecasting and Estimation , 2005 .

[32]  W. C. Lennox,et al.  Groups and neural networks based streamflow data infilling procedures , 2001 .