Comparing the Efficiency of Sediment Rating Curve and ANN Models in Estimating River Bed-load

Evaluation and selection of the most appropriate methods for bed-load estimation is necessary because of the sampling difficulties and inaccurate estimations of the empirical equations. The present study aims to compare the efficacy of ANN and SRC statistical models to estimate the bed-load sediments. Collecting bed-load measurement data and their respective discharges, 5 stations with the highest number of samples were selected. Then, SRC and ANN models were developed. Finally, the estimations of two models were compared with observed values using correlation coefficient and RMSE indices. The results showed that bed-load has been increased by increasing the amount of flow rate in all hydrometric stations. Significant level of difference between observed and estimated values ​​of the ANN model (0.592) is greater than the SRC model (0.144). This means that observed and estimated values ​​of the ANN model are closer together than SRC model, so estimations of ANN model are more accurate. The Root Mean Square Error index (RMSE) for ANN model is also smaller than the SRC model in all stations, so that the sum of five stations RMSE for ANN and SRC models were 2505.7 and 5195.3 respectively. The correlation coefficients of the ANN model are greater than SRC model in all stations. The greater average of correlation coefficients of five stations using ANN model (0.765) than the SRC model (0.503) indicate that ANN model has more accurate estimations. Finally, ANN model was selected as more appropriate model to estimate bed-load sediments. Regarding the measurement problems of bed-load, our results can lead to making more accurate estimations of bed-load and total sediment load.