Evaluation the Performance of Artificial Neural Network Method and Deep Learning Method for Prediction of Bed Load in Gravel-Bed Rivers

Document Type : Complete scientific research article

Authors

1 Corresponding Author, Professor, Dept. of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.

2 M.Sc. Graduate of Water and Hydraulic Structures Engineering, Dept. of Civil Engineering, University of Tabriz, Tabriz, Iran.

Abstract

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.

Keywords

References

1.Batalla, R.J. 1997. Evaluating bed-material transport equations from field measurements in a sandy gravel-bed river. Earth Surf. Process, Land. 21: 121-130.
2.Martin, Y., and Ham, D. 2005. Testing bed load transport formulae using morphologic transport estimates and
field data: lower Fraser River, British Columbia. Earth Surf. Process. Landf.30: 1265-1282.
3.Gomez, B., and Church, M. 1989. An assessment of bed load sediment transport formulae forgravel bed rivers. Water Resour. 25: 6. 1161-1186.
4.Haddadchi, A., Omid, M.H., and Dehghani, A.A. 2011. Assessment of bedload predictors based on bampling
in a gravel bed river. Journal of Hydrodynamic. 24: 1. 145-151.
5.Sasal, M., Kashyap, S., Rennie, C.D.,and Nistor, I. 2009. Artificial neural network for bedload estimation in alluvial rivers. Journal of Hydraulic Research.47: 2. 223-232.
6.Yang, C.T., Marsooli, R., and Aalami, M.T. 2009. Evaluation of total load sediment transport formulas using ANN. International Journal of Sediment Research. 24: 3. 274-286.
7.Kitsikoudis, V., Sidiropoulos, E., and Hrissanthou, V. 2014. Machine learning utilization for bed load transport in gravel-bed rivers. Water Resources Management. 28: 11. 3727-3743.
8.Mosfaei, J., Salehpour Jam, A., and Tabatabai, M.R. 2017. Comparison of the efficiency of sediment rating curves model and artificial neural network in the study of river bed load. Geography and environmental sustainability. 24: 7. 33-44. (In Persian)
9.Zaytar, M.A., and El Amrani, C. 2016. Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. International Journal of Computer Applications. 143: 11. 7-11.
10.Baroni, A., and Ziarati, K. 2019. Modeling of minimum temperature in Fars province using LSTM recurrent neural network model. 4th International Congress of Developing Agriculture, Natural Resources, Environmentand Tourism of Iran, Tehran, Iran.(In Persian)
11.Kaveh, K., Kaveh, H., Duc Bui, M., and Rutschmann, P. 2021. Long short‑term memory for predicting daily suspended sediment Concentration. Engineering with Computers. 37: 1. 2013-2027.
12.AlDahoul, N., Essam, Y., Kumar, P., Ahmed, A.N., Sherif, M., Sefelnasr, A., and Elshafie, A. 2021. Suspended sediment load prediction using long short‑term memory neural network. Scientific Reports. 11: 7826.
13.King, J.G., Emmett, W.W., Whiting, P.J., Kenworthy, R.P., and Barry, J.J. 2004. Sediment transport Data and Related Information for Selected Coarse-Bed Streams and Rivers in Idaho, Gen. Tech. Rep. RMRS-GTR-131. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station, 26p.
14.Meyer-Peter, E., and Müller, R. 1948. Formulas for bed-load transport. In Proceedings of the 2nd Meeting of the International Association for Hydraulic Structures Research. pp. 39-64.
15.Schoklitsch, A. 1950. Handbuch des wasserbaues. Springer, New York, 478p.
16.Brown, C.B. 1950. Sediment transportation. Engineering hydraulic, edited by H. Rouse, John Wiley,New York. pp. 769-857.
17.Rottner, J. 1959. A formula for bedload transportation. La Houille Blanche. 3: 4. 301-307.
18.Bagnold, R.A. 1980. An empirical correlation of bed load transport rates in flumes and natural rivers. Proc. Roy. Soc. Lond. Ser. A. 372: 453-473.
19.Parker, G., Klingeman, P.C., and McLean, D.G. 1982. Bedload and the size distribution of paved gravel-bed streams. Journal of the Hydraulics Division. 108: 4. 544-571.
20.VanRijn, L.C. 1984a. Sediment transport, Part I: Bedload transport. Journal of Hydraulic Engineering.110: 10. 1431-1456.
21.Wilcock, P.R., and Crowe, J.C. 2003. Surface-based transport model for mixed-size sediment. Journal of Hydraulic Engineering. 129: 2. 120-128.
22.Wong, M., and Parker, G. 2006. Reanalysis and correction of bed-load relation of Meyer- Peter and Müller using their own database. Journal of Hydraulic Engineering. 132: 11. 1159-1168.
23.Bhattacharya, B., Price, R.K., and Solomatine, D.P. 2007. Machine learning approach to modeling sediment transport. Journal of Hydraulic Engineering. 133: 4. 440-450.
24.Igor Aizenberg, Naum N., Aizenberg, Joos P.L., Vandewalle. 2000. Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer Science and Business Media, New York, 276p.
25.Graves, A., and Schmidhuber, J. 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks. 12: 5-6.
26.Bengio, Y., Simard, P., and Frasconi, P. 1994. Learning long-term dependencies with gradient descent is difficult.
IEEE Transactions on Neural Networks. 5: 2. 157-166.
27.Gers, F.A., Schmidhuber, J., and Cummins, F. 2000. Learning to forget: Continual prediction with LSTM. Neural Computation. 12: 10. 2451-2471.
28.Legates, D.R., and McCabe, G.J. 1999. Evaluating the Use of “Goodness-of-Fit” Measures in Hydrologic and Hydroclimatic Model Validation.Water Resources Research. 35: 233-241.
29.López, R., Vericat, D., and Batalla, R.J. 2013. Evaluation of bed load transport formulae in a large regulated gravel bed river: the lower Ebro (NE Iberian Peninsula). Journal of Hydrology.510: 164–181.
Journal of Water and Soil Conservation
Volume 29, Issue 1
May 2022
Pages 75-94

Files

Share

How to cite

Statistics