Oct-10 - Canadian Journal of Pure and Applied Sciences

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Chowdhary and Shrivastava 1279 Fig. 3. Comparison of the observed & the FFNN estimated daily discharge during training period. Fig. 4. Comparison of the observed & the FFNN estimated daily discharge during testing period. Fig. 5. Comparison of the observed & the RBFNN simulated daily discharge during training period. Fig. 6. Comparison of the observed & the RBFNN simulated daily discharge during testing period. Fig. 7. Scatter plot between observed & the FFNN estimated daily discharge during training period. Fig. 8. Scatter plot between observed & the FFNN estimated daily discharge during testing period. Fig. 9. Scatter plot between observed & the RBFNN simulated daily discharge during training period. Fig.10. Scatter plot between observed & the RBFNN simulated daily discharge during testing period. changing the values of spread in RBFNN from 0.05 to 1.00 simulation runs were performed to predict the daily discharge and thereafter the best RBFNN model was selected. The RBFNN simulated daily discharge values are plotted continuously irrespective of missing years up to 726 days (i.e., 2 water years) in figure 6 during testing, 3053 (i.e., 8 water years) in figure 5 during training respectively The daily hydrographs and the linear agreement between the observed and the ANN simulated using FFNN and RBFNN streamflows are depicted in figures 7 & 8 and figures 9 & 10 respectively.
1280 River discharge prediction using artificial neural network Table1a. SEONATH DISCHARGE. Year Mean Standard Deviation Skewness Kurtosis Covariance Max Min 2000-2001 78.4117 268.67 7.262 64.08 72180 2870 0 2001-2002 194.866 430.73 3.711 19.8 185000 3372.63 0.1 2002-2003 93.64 280.144 5.2 39.1362 78400 2914.8 0.04 2003-2004 332.78 657.168 3.209 15.5597 413870 4724.8 0.84 2004-2005 160.1 365.6419 3.8343 19.5274 133690 2722.6 0.65 2005-2006 372.163 1049.9 4.9512 31.6813 1102300 9105.7 0.83 2006-2007 248.0292 830.633 6.0083 43.9904 689950 7848.8 0.76 2007-2008 247.9557 701.9785 6.2978 49.6811 492770 6867.07 2.02 2008-dec2008 185.1562 338.1807 6.8233 64.971 114370 3625.7 2.55 Table1b. SEONATH STAGE. Year Mean Standard Deviation Skewness Kurtosis Covariance Max Min 2000-2001 236.07 0.895 3.65 22.31 0.8023 242.86 235 2001-2002 236.55 1.347 2.18 8.14 1.8612 244.35 236.57 2002-2003 237.13 0.933 2.89 11.84 0.871 242.78 236.54 2003-2004 237.93 1.675 2.04 7.53 2.8071 246.63 236.77 2004-2005 237.47 1.416 2.23 8.22 1.3136 242.95 236.55 2005-2006 237.82 2.022 3.04 13.66 4.0909 249.7 236.56 2006-2007 237.54 1.638 3.86 20.46 2.6856 248.25 236.65 2007-2008 237.69 1.523 3.25 17.91 2.3209 248.05 236.67 2008-dec2008 237.56 0.871 3.64 24.69 0.7597 244.3 236.66 CONCLUSIONS Feed forward neural networks trained with backpropagation usually start with a large network and proceeds by removing weights to which sensitivity of the error is minimal. The optimal architectures of ANN for the present investigation are 5-30-1 for feed forward neural networks and 5-20-1 for radial basis function neural networks with different range of spread values. This is similar to the idea of calibration that is an integral part of most of the time series modelling in the field of hydrology. The results show that the radial basis function neural networks are found to produce an accurate forecast of daily streamflow when compared to feed forward neural networks to the particular gauge/discharge station. The prediction accuracy of the ANN based model is highly dependent on many issues associated with network structure identification and network parameters such as interconnected weights, learning rate, momentum coefficient, and the number of epochs needed for optimization. This study aims to improve the model performance by explicitly incorporating hydrological a priori knowledge, reducing the network sensitivity to input errors and changing the training objective function. The results of this case study, demonstrated in the Seonath nandghat G/D station at Seonath River Basin under experimentation, were encouraging. To improve the performance of the ANN models, the incorporation of other climatic variables such as rainfall and temperature into the input data set and use of recurrent and modular neural networks, may be a good focus for the future direction of research work. REFERENCES Bhattacharya, B. and Solomatine, DP. 2000. Application of artificial neural networks in stage discharge relationship, Proc. of 4 th International conference on Hydro informatics, lowa City, USA. Birikundavyi, S., Labib, R., Trung, HT. and J. Rousselle, J. 2002. Performance of neural network in daily streamflow forecasting. J. Hydrologic Engg., ASCE. 7 (5):392-398. Burian, JS., Durrans, SR., Nix, SJ. and Pitt, RE. 2001. Training artificial neural networks to perform rainfall disaggregation. J. Hydrologic Engg., ASCE. 6(1):43-51. Coulibaly, P., Anctil, F. and Bobee, B. 2001. Multivariate reservoir inflow forecasting using temporal neural networks. J. Hydrologic Engg., ASCE. 6(5): 367-376.
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