نوع مقاله : مقاله کامل علمی پژوهشی
1 نویسنده مسئول، استاد گروه مهندسی آب، دانشکده عمران، دانشگاه تبریز، تبریز، ایران.
2 دانشآموخته کارشناسیارشد مهندسی آب و سازههای هیدرولیکی، دانشکده عمران، دانشگاه تبریز، تبریز، ایران.
عنوان مقاله [English]
Background and objectives: Assessing and estimating sediment transport from a long time ago is one of the major issues for hydraulic and river engineers. Determining the amount of bed load carried in rivers depends on various factors, and this factor has complicated this phenomenon. Studies on different rivers show that the amount of bed load in different hydraulic and hydrological conditions is different. In addition, the physical properties of bed load particles have a significant effect on the accuracy of model prediction, On the other hand, despite the emphasis on the unreliability of experimental equations that have been extended over a specific area, unfortunately, limited studies have been conducted on temporary changes in bed load. Therefore, Investigating the predictability of this phenomenon is of great importance. In this study, we will try to estimate the bed load in gravel bed rivers using classical and intelligent methods.
Materials and methods: Machine learning methods due to their high accuracy in predicting various issues have been noted in recent years . Therefore, in the present study, two methods of classical artificial neural network (ANN) and deep learning of long short-term memory (LSTM), which is a kind of artificial neural network with layers and amplification algorithms to improve network performance; have been used to predict the bed load of 19 gravel-bed rivers. To define suitable models for networks, the results of 10 experimental formulas in bed load prediction have been evaluated and the parameters of superior formulas have been used as the input of intelligent networks.
Results: The results showed that all experimental formulas had very poor results; As most formulas have predicted the bed load with a Discrepancy index of r greater than 100. However, machine methods with input parameters obtained from experimental formulas have acceptable accuracy in predicting bed load. and in comparison with machine methods, LSTM method has provided more accurate results than ANN method. Finally, the model related to the parameters of Begnold formula in LSTM method with DC= 0.900 and RMSE= 0.024 for the test data is the best model obtained from this research and The average diameter of sediment particles (D50), which is a common parameter of the top three models, has been selected as the most effective parameter in predicting bed load.
Conclusion: Despite the very poor performance of experimental formulas in predicting sediment transport, intelligent networks with input parameters derived from experimental formulas have had good results. Also, LSTM network is more efficient than artificial neural network (ANN) in predicting bed load transfer, which indicates that Maintaining training memory during the training process and adding reinforcement layers to the network improves network performance and increases network accuracy in subsequent training.