Abstract
River sedimentation is a universal issue in a river catchment. It can affect the reservoir ability, the river flow, and dam structure including the hydropower capacity. Therefore, having multi-step ahead forecasting for the sediment load is beneficial in terms of research and applications. This study discusses and presents a case study in multi-step ahead forecasting for the sediment load using non-linear autoregressive with exogenous inputs (NARX) neural networks. We use sediment data that was recorded from 8 locations in the Ringlet reservoir (upstream sections) in Malaysia. The results suggest that the NARX neural networks have good capability to do multi-step ahead forecasting for sediment load in a recursive way (closed-loop mode) based on its past values and the past values of suspended solid and discharge. The model is evaluated with performance metrics yielding NSE = 0.99 (Nash–Sutcliffe efficiency coefficient) for both the training and test dataset, and RMSE (root means square error) of 0.22 and 0.25, respectively, training and test dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A.Z. Abdul Razad, L.M. Sidek, K. Jung, H. Basri, Reservoir inflow simulation using Mike Nam rainfall-runoff model. J. Eng. Sci. Technol. 13(12), 4206–4225 (2018)
H.A. Afan, A. El-Shafie, Z.M. Yaseen, M.M. Hameed, W.H.M. Wan Mohtar, A. Hussain, ANN based sediment prediction model utilizing different input scenarios. Water Resour. Manag. 29(4), 1231–1245 (2014). https://doi.org/10.1007/s11269-014-0870-1
V.J. Alarcon, Hindcasting and forecasting total suspended sediment concentrations using a NARX neural network. Sustain. 13(1), 1–18 (2021). https://doi.org/10.3390/su13010363
H. Bouzeria, A.N. Ghenim, K. Khanchoul, Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria. J. Water l. Dev. 33(1), 47–55 (2017). https://doi.org/10.1515/jwld-2017-0018
M.B. Gasim, S. Surif, M.E. Toriman, S.A. Rahim, R. Elfithri, P.I. Lun, Land-use change and climate-change patterns of the Cameron highlands, Pahang, Malaysia. Arab World Geogr. 12(1–2), 51–61 (2009). https://doi.org/10.5555/ARWG.12.1-2.L2P14J2833G2Q4L7
W.W. Guo, H. Xue, Crop yield forecasting using artificial neural networks: a comparison between spatial and temporal models. Math. Probl. Eng. 2014(January), 2014 (2014). https://doi.org/10.1155/2014/857865
G. Hayder, M.I. Solihin, K.F. Bin Kushiar, A performance comparison of various artificial intelligence approaches for estimation of sediment of river systems. J. Ecol. Eng. 22(7), 20–27 (2021). https://doi.org/10.12911/22998993/137847
S. Kumar, A. Pandey, B. Yadav, Assessing the applicability of TMPA-3B42V7 precipitation dataset in wavelet-support vector machine approach for suspended sediment load prediction. J. Hydrol. 550, 103–117 (2017). https://doi.org/10.1016/j.jhydrol.2017.04.051
A.M. Melesse, S. Ahmad, M.E. McClain, X. Wang, Y.H. Lim, Suspended sediment load prediction of river systems: an artificial neural network approach. Agric. Water Manag. 98(5), 855–866 (2011). https://doi.org/10.1016/J.AGWAT.2010.12.012
B. Mohammadi, Y. Guan, R. Moazenzadeh, M. Jafar, S. Safari, Catena Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation. Catena, December 2019, 105024 (2020). https://doi.org/10.1016/j.catena.2020.105024
S. Nivesh, P. Kumar, Modelling river suspended sediment load using artificial neural network and multiple linear regression: Vamsadhara River Basin, India. Int J. Chem. Stud. 5(5), 337–344 (2017)
M. Pena, A. Vazquez-Patino, D. Zhina, M. Montenegro, A. Aviles, Improved rainfall prediction through nonlinear autoregressive network with exogenous variables: a case study in Andes High Mountain region. Adv. Meteorol. 2020 (2020). https://doi.org/10.1155/2020/1828319
R. Sarkar, S. Julai, S. Hossain, W.T. Chong, M. Rahman, A comparative study of activation functions of NAR and NARX neural network for long-term wind speed forecasting in Malaysia. Math. Probl. Eng. 2019 (2019). https://doi.org/10.1155/2019/6403081
L. Sidek, Hydropower reservoir for flood control: A case study on ringlet. J. Flood Eng. 4(June 2013), 87–102 (2013)
S. Thapa et al., Snowmelt-driven streamflow prediction using machine learning techniques (LSTM, NARX, GPR, and SVR). Water (Switzerland) 12(6) (2020). https://doi.org/10.3390/w12061734
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Solihin, M.I., Hayder, G., Maarif, H.AQ., Khan, Q. (2023). Multi-Step Ahead Time-Series Forecasting of Sediment Load Using NARX Neural Networks. In: Salih, G.H.A., Saeed, R.A. (eds) Sustainability Challenges and Delivering Practical Engineering Solutions. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-26580-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-26580-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26579-2
Online ISBN: 978-3-031-26580-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)