TY - JOUR ID - 74053 TI - Comparison on artificial neural network and sediment rating curve models for simulating of suspended sediment load;case study Shahrood watershed JO - Irrigation and Water Engineering JA - IWE LA - en SN - 2251-7359 AU - Mohamadi, Sedigheh AD - Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran Y1 - 2017 PY - 2017 VL - 7 IS - 3 SP - 32 EP - 46 KW - Key words: Multi-Layer Perceptron KW - Radial Base Function KW - Shahrood KW - NASH coefficient KW - Sediment rating curve DO - N2 - This research was conducted to compare the efficiency of some simulation  models including sediment rating curves and artificial neural networks  for  simulating the  suspended sediment load amount. Optimized model basis of flow discharge in Shahrood watershed upon the  hydrometric stations including Glinak, Baghkalaye, Loshan and Rajayi dasht was represented. In order to simulate the suspended sediment load we compared  one linear rating curve and artificial neural network with multi-layer perceptron and radial base function models. Then performance evaluation these models was carried out by NASH and RMSE criteria..The results showed that artificial neural network  with multi-layer perceptron method  in comparison on  sediment rating curve model in all of these stations simulated  better models . So that artificial neural network with sigmoid triggering function in Glinak and Rajayi dasht stations with RMSE as 1.033 and 0.825 ton/day and NASH as 0.84 and 0.839 and this model with tansigmoid triggering function in Baghkalaye and Loshan stations with RMSE as 0.799 and 0.883 ton/day and NASH as 0.772 and 0.895, respectively, have the better efficiency for simulating of suspended sediment load amount. Also comparison of two neural network models showed that MLP model is better than RBF model for simulating of suspended sediment load amount. The only benefit of RBF networks is less time needed for training.   UR - https://www.waterjournal.ir/article_74053.html L1 - https://www.waterjournal.ir/article_74053_550519ddc5654c584cce6a0feb4c6b94.pdf ER -