Modelling of Daily Suspended Sediment Concentration Using FFBPNN and SVM Algorithms

Document Type : Regular Article

Authors

1 Assistant Professor, Faculty of Civil Engineering, MIT, Muzaffarpur, Bihar, Research Scholar IIT (BHU), Varanasi, India

2 Assistant Professor, Faculty of Electronics and Communication Engineering, Oriental Group of Institutions, India

3 Assistant Professor, Faculty of Civil Engineering, SIT, Sitamarhi, Bihar, India

4 Professor, Faculty of Civil Engineering, IIT (BHU), Varanasi, India

Abstract

The river is an essential resource of fresh water on the earth, and its management is very challenging. Sedimentation and erosion is a very complex process of the river system. Suspended sediment concentration (SSC) plays a key role in this process. Therefore, water resources planning and management are essential for this. Generally, the sediment concentration estimated by direct measurement, but this process is costly and cannot apply in all rivers. It is essential to develop some technology that can predict the suspended sediment concentration. So, in this study, a feed forward back propagation neural network (FFBPNN) and support vector machine (SVM) were used to predict the suspended sediment concentration. One year of daily data was collected from the river Ganga at Varanasi cross-section. The performance of the model estimated for training and validation stages based on root mean square error (RSME), Coefficient of correlation (R) and Nash–Sutcliffe model efficiency (NSE). The performance of applied model indicated that FFBPNN (RSME = 176.2, R = 0.955 and NSE = 0.912) for validation is more precise for suspended sediment load prediction than SVM (RSME = 222.1, R = 0.930 and NSE = 0.864). This study shows that the soft computing technique is a robust tool for SSC prediction.

Keywords

Main Subjects

References

[1]     Najafzadeh M, Saberi-Movahed F. GMDH-GEP to predict free span expansion rates below pipelines under waves. Mar Georesources Geotechnol 2019;37:375–92. doi:10.1080/1064119X.2018.1443355.
[2]     Najafzadeh M, Ghaemi A. Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods. Environ Monit Assess 2019;191:380. doi:10.1007/s10661-019-7446-8.
[3]     Senthil Kumar AR, Ojha CSP, Goyal MK, Singh RD, Swamee PK. Modeling of Suspended Sediment Concentration at Kasol in India Using ANN, Fuzzy Logic, and Decision Tree Algorithms. J Hydrol Eng 2012;17:394–404. doi:10.1061/(ASCE)HE.1943-5584.0000445.
[4]     Najafzadeh M, Ghaemi A, Emamgholizadeh S. Prediction of water quality parameters using evolutionary computing-based formulations. Int J Environ Sci Technol 2019;16:6377–96. doi:10.1007/s13762-018-2049-4.
[5]     Nagy HM, Watanabe K, Hirano M. Prediction of Sediment Load Concentration in Rivers using Artificial Neural Network Model. J Hydraul Eng 2002;128:588–95. doi:10.1061/(ASCE)0733-9429(2002)128:6(588).
[6]     CIĞIZOĞLU HK. Suspended sediment estimation and forecasting using artificial neural networks. Turkish J Eng Environ Sci 2001;26:16–26.
[7]     Bisoyi N, Gupta H, Padhy NP, Chakrapani GJ. Prediction of daily sediment discharge using a back propagation neural network training algorithm: A case study of the Narmada River, India. Int J Sediment Res 2019;34:125–35. doi:10.1016/j.ijsrc.2018.10.010.
[8]     Wang YM, Kerh T, Traore S. Neural networks approaches for modelling river suspended sediment concentration due to tropical storms. Glob NEST J 2009;11:457–66.
[9]     Doğan E, Yüksel İ, Kişi Ö. Estimation of total sediment load concentration obtained by experimental study using artificial neural networks. Environ Fluid Mech 2007;7:271–88. doi:10.1007/s10652-007-9025-8.
[10]    Jain SK. Development of Integrated Sediment Rating Curves Using ANNs. J Hydraul Eng 2001;127:30–7. doi:10.1061/(ASCE)0733-9429(2001)127:1(30).
[11]    Partal T, Cigizoglu HK. Estimation and forecasting of daily suspended sediment data using wavelet–neural networks. J Hydrol 2008;358:317–31. doi:10.1016/j.jhydrol.2008.06.013.
[12]    TAYFUR G. Artificial neural networks for sheet sediment transport. Hydrol Sci J 2002;47:879–92. doi:10.1080/02626660209492997.
[13]    Cigizoglu HK. Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv Water Resour 2004;27:185–95. doi:10.1016/j.advwatres.2003.10.003.
[14]    Cigizoglu HK, Kişi Ö. Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Hydrol Res 2005;36:49–64.
[15]    Kerem Cigizoglu H, Kisi Ö. Methods to improve the neural network performance in suspended sediment estimation. J Hydrol 2006;317:221–38. doi:10.1016/j.jhydrol.2005.05.019.
[16]    Tayfur G, Guldal V. Artificial neural networks for estimating daily total suspended sediment in natural streams. Hydrol Res 2006;37:69–79.
[17]    Kisi O, Haktanir T, Ardiclioglu M, Ozturk O, Yalcin E, Uludag S. Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Adv Eng Softw 2009;40:438–44. doi:10.1016/j.advengsoft.2008.06.004.
[18]    Chiang J-L, Tsai Y-S. Suspended sediment load estimate using support vector machines in Kaoping river basin. 2011 Int Conf Consum Electron Commun Networks, IEEE; 2011, p. 1750–3. doi:10.1109/CECNET.2011.5769267.
[19]    ÇIMEN M. Estimation of daily suspended sediments using support vector machines. Hydrol Sci J 2008;53:656–66. doi:10.1623/hysj.53.3.656.
[20]    Kisi O. Modeling discharge-suspended sediment relationship using least square support vector machine. J Hydrol 2012;456–457:110–20. doi:10.1016/j.jhydrol.2012.06.019.
[21]    Najafzadeh M, Zeinolabedini M. Prognostication of waste water treatment plant performance using efficient soft computing models: An environmental evaluation. Measurement 2019;138:690–701. doi:10.1016/j.measurement.2019.02.014.
[22]    Himanshu SK, Pandey A, Yadav B. Ensemble Wavelet-Support Vector Machine Approach for Prediction of Suspended Sediment Load Using Hydrometeorological Data. J Hydrol Eng 2017;22:05017006. doi:10.1061/(ASCE)HE.1943-5584.0001516.
[23]    Mahanta C, Bora T, Pathak N. Evaluation of Sediment Flux in a Part of the Brahmaputra River and Application of ANN and Linear Regression Models. World Environ Water Resour Congr 2007, Reston, VA: American Society of Civil Engineers; 2007, p. 1–13. doi:10.1061/40927(243)397.
[24]    Cobaner M, Unal B, Kisi O. Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. J Hydrol 2009;367:52–61. doi:10.1016/j.jhydrol.2008.12.024.
[25]    Kakaei Lafdani E, Moghaddam Nia A, Ahmadi A. Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol 2013;478:50–62. doi:10.1016/j.jhydrol.2012.11.048.
[26]    Yadav A, Chatterjee S, Equeenuddin SM. Suspended sediment yield estimation using genetic algorithm-based artificial intelligence models: case study of Mahanadi River, India. Hydrol Sci J 2018;63:1162–82. doi:10.1080/02626667.2018.1483581.
[27]    He Z, Wen X, Liu H, Du J. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 2014;509:379–86. doi:10.1016/j.jhydrol.2013.11.054.
[28]    CIĞIZOĞLU HK. Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves. Turkish J Eng Environ Sci 2001;26:27–36.
[29]    Rajaee T, Nourani V, Zounemat-Kermani M, Kisi O. River Suspended Sediment Load Prediction: Application of ANN and Wavelet Conjunction Model. J Hydrol Eng 2011;16:613–27. doi:10.1061/(ASCE)HE.1943-5584.0000347.
[30]    BOUKHRISSA ZA, KHANCHOUL K, LE BISSONNAIS Y, TOURKI M. Prediction of sediment load by sediment rating curve and neural network (ANN) in El Kebir catchment, Algeria. J Earth Syst Sci 2013;122:1303–12. doi:10.1007/s12040-013-0347-2.
[31]    Kişi Ö. River suspended sediment concentration modeling using a neural differential evolution approach. J Hydrol 2010;389:227–35. doi:10.1016/j.jhydrol.2010.06.003.
[32]    Khan MYA, Tian F, Hasan F, Chakrapani GJ. Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India. Int J Sediment Res 2019;34:95–107. doi:10.1016/j.ijsrc.2018.09.001.
[33]    Shivhare N, Rahul AK, Omar PJ, Chauhan MS, Gaur S, Dikshit PKS, et al. Identification of critical soil erosion prone areas and prioritization of micro-watersheds using geoinformatics techniques. Ecol Eng 2018;121:26–34. doi:10.1016/j.ecoleng.2017.09.004.
[34]    Shivhare N, Rahul AK, Dwivedi SB, Dikshit PKS. ARIMA based daily weather forecasting tool: A case study for Varanasi. MAUSAM 2019;70:133–40.
[35]    Rahul AK, Shivhare N, Dwivedi SB, Dikshit PKS. Estimation of behavioral change of SSC of bed profile in the river using ADCP. Arab J Geosci 2020;13:105. doi:10.1007/s12517-020-5061-1.
[36]    Kumar S, Kumar R, Mandal S, Rahul AK. The Prediction of Buckling Load of Laminated Composite Hat-Stiffened Panels Under Compressive Loading by Using of Neural Networks. Open Civ Eng J 2018;12:468–80. doi:10.2174/1874149501812010468.
[37]    Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273–97.
[38]    Ben-Hur A, Horn D, Siegelmann HT, Vapnik V. Support vector clustering. J Mach Learn Res 2001;2:125–37.
[39]    ALP M, CIGIZOGLU H. Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environ Model Softw 2007;22:2–13. doi:10.1016/j.envsoft.2005.09.009.
[40]    Goh AT. Some civil engineering applications of neural networks. Proc Inst Civ Eng Build 1994;104:463–9.
[41]    Rafiq M., Bugmann G, Easterbrook D. Neural network design for engineering applications. Comput Struct 2001;79:1541–52. doi:10.1016/S0045-7949(01)00039-6.
[42]    Nash JE, Sutcliffe JV. River flow forecasting through conceptual models part I — A discussion of principles. J Hydrol 1970;10:282–90. doi:10.1016/0022-1694(70)90255-6.

Files

Cited by

History

  • Receive Date: 25 April 2021
  • Revise Date: 19 July 2021
  • Accept Date: 20 July 2021

Share

How to cite

Statistics