JWSS - Journal of Water and Soil Science

---

The model in this research was created based on the Artificial Neural Network (ANN) and calibrated in the Sefid-rood dam basin (excluding Khazar zone). This research was done by gathering and selecting peak flows of hydrographs from 12 sub basins, the concentration time of which was equal to or less than 24 hours and was caused only by rainfall. From all the selected sub basins, totally 661 hydrographs were prepared and their peak flows data wes used to make prediction model. The input variables of the model consisted of the depth of daily flooding rainfalls, and so the five days before rainfall of every peak flow, the area of sub basins, the main stream length, the slope of 10-85 percent of main stream, the median height of sub basins, the area of geological formations and rock units, classified at three hydrological groups of I, II, III, the base flow, and output variable was only peak flow. By using Feed Forward Artificial Neural Network with training method of back propagation error the function approximation of inputs to output was created by passing the three processes of training (learning), testing and validation. So based on that data and variables, the Multivariable Linear Regression model was created. The comparison of observed peak flows, based on validation data package, showed that the statistical parameters of (R²) coefficient and Fisher’s test parameter coefficient (F) for ANN model and MLR respectively were 0.84, 33.66 and 0.33, 3.60, indicating the superiority of ANN to traditional methods.

---

Direct measurement of discharge in rivers is time-consuming and costly, and sometimes, impossible under flood conditions because of the high speed of water, its transitory nature, and the existence of different floaters along the water. Therefore, the discharge-stage relation, known as Discharge Rating Curve is used. Moreover, to design hydraulic constructions, the maximum flood discharge and its maximum height are required. Therefore, to calculate the flood discharges, one should extend the discharge rating curve by using appropriate methods. In this study, in order to determine the best method for the extension of discharge-stage curve, and to estimate the corresponding discharge with high stages, the logarithmic method, the Manning method, the Chezy method, and the Area-Velocity method in 13 hydrometric stations at the Karkheh watershed in Lorestan province were compared. Data measured at each station were gathered for a ten-year statistical period. Results of calculating the Root Mean Square Error (RMSE) and the Mean Bias Error (MBE) for each method showed that the logarithmic method was more accurate than other methods, and it was more appropriate for the extension of the curve at the low average discharge stations. The Area-Velocity method, after the logarithmic method, especially at the stations with higher average discharge showed good results. The Manning and Chezy methods showed the least accuracy.