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Estimation of suspended sediment load using three neural network algorithms in Ramganga River catchment of Ganga Basin, India

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Abstract

The information on suspended sediments of river is considered to be crucial for issues concerning water management and the environment. The abrupt quantity and nature of sediment loads can be best studied by simultaneously considering the governing variables contributing towards this physical phenomenon. Artificial Neural Network (ANN) is one of the suitable data-mining technique which helps in carrying out the modelling of this phenomenon. In this study, ANNs are employed to approximate the monthly mean suspended sediment load for Ramganga River. Three simulations with rainfall and water discharge data were carried out to predict the suspended sediment load. In terms of the selected performance criteria, three algorithms were evaluated and the results so obtained are presented. It has been found that rainfall values were not sufficient to correctly predict the suspended sediment load. However, considering water discharge values as input improves the performance of all the three considered algorithms.

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References

  • Alok A, Patra KC, Das SK (2013) Prediction of discharge with elman and cascade neural networks. Res J Recent Sci 2:279–284

    Google Scholar 

  • Alp M, Cigizoglu HK (2007) Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environ Model Softw 22(1):2–13

    Article  Google Scholar 

  • Arora MK (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 25(3)559–572

    Article  Google Scholar 

  • ASCE (2000a) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5(2):115–123

    Article  Google Scholar 

  • ASCE (2000b) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrol Eng 5(2):124–137

    Article  Google Scholar 

  • Bayram A, Kankal M, Tayfur G, Onsoy H (2014) Prediction of suspended sediment concentration from water quality variables. Neural Comput Appl 24:1079–1087

    Article  Google Scholar 

  • Bhattacharya B, Price R, Solomatine D (2005) Data-driven modelling in the context of sediment transport. Phys Chem Earth 30:297–302

    Article  Google Scholar 

  • Brikundavyi S, Labib R, Trung HT, Rousselle J (2002) Performance of neural networks in daily streamflow forecasting. J Hydrol Eng 7(5):392–398

    Article  Google Scholar 

  • Broomhead D, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355

    Google Scholar 

  • Chang FJ, Chang LC, Kao HS, Wu GR (2010) Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network. J Hydrol 384(s 1–2):118–129

    Article  Google Scholar 

  • Chen L, Wang Y (2014) Application of radial basis function neural network on the prediction of urban built-up area. Appl Mech Mater 556–562:5308–5311

    Google Scholar 

  • Cigizoglu K (2002a) Forecasting of meteorologic data by artificial neural networks. In: Rutkowskiand JK (eds) Advances in soft computing (Proc. Sixth Int. Conf. on Neural Networks and Soft Computing, Zakopane, Poland, 11–15 June 2002), pp 820–824, Physica-Verlag, Heidelberg

  • Cigizoglu HK (2002b) Intermitting river flow forecasting by artificial neural networks. In: Hassanizadeh SM, Schotting RJ, Gray WG, Pinder GF (eds) XIV. Int. Conf. on Computational Methods in Water Resources (Proc. Delft, The Netherlands, 23–28 June 2002), pp 1653–1660. Elsevier Publ. no. 47, Amsterdam

  • Cigizoglu HK (2003a) Incorporation of ARMA models into flow forecasting by artificial neural networks. Environmetrics 14(4):417–427

    Article  Google Scholar 

  • Cigizoglu HK (2003b) Estimation, forecasting and extrapolation of flow data by artificial neural networks. Hydrol Sci J 48(3):349–361

    Article  Google Scholar 

  • Cigizoglu HK (2005) Generalized regression neural network in monthly flow forecasting. Civil Eng Environ Syst 22(2):71–84

    Article  Google Scholar 

  • Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37(2):63–68

    Article  Google Scholar 

  • Cigizoglu HK, Kisi O (2005a) Flow prediction by two back propagation techniques using k-fold partitioning of neural network training data. Nord Hydrol 36(1):1–16

    Article  Google Scholar 

  • Central Water Commission (CWC) (2012) Environmental evaluation study of Ramganga major irrigation project, vol 1. Central Water Commission, Ministry of Water Resources, Government of India, New Delhi

    Google Scholar 

  • Daityari S, Khan MYA (2017) Temporal and spatial variations in the engineering properties of the sediments in Ramganga River, Ganga Basin, India. Arab J Geosci. https://doi.org/10.1007/s12517-017-2915-2

    Article  Google Scholar 

  • Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229–240

    Article  Google Scholar 

  • Dawson DW, Wilby R (1998) An artificial neural network approach to rainfall–runoff modelling. Hydrol Sci J 43(1):47–65

    Article  Google Scholar 

  • Eberhart RC, Dobbins RW (1990) Neural network PC tools: a practical guide. Academic, San Diego

    Google Scholar 

  • El-Bakyr MY (2003) Feed forward neural networks modeling for K–P interactions. Chaos Solitions Fractals 18(3):995–1000

    Article  Google Scholar 

  • Fernando DAK, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3(3):203–209

    Article  Google Scholar 

  • Govindaraju SR (2000) Artificial neural network in hydrology. II: hydrologic applications. J Hydrol Eng 5(2):124–137

    Article  Google Scholar 

  • Gupta RP, Joshi BC (1990) Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment. Himal Eng Geol 28:119–131

    Article  Google Scholar 

  • Hagan MT, Menhaj MB (1994) Training feed forward techniques with the Marquardt algorithm. IEEE Trans Neural Networks 5(6):989–993

    Article  Google Scholar 

  • Hidayat H, Hoitink AJF, Sassi MG, Torfs PJJF (2014) Prediction of discharge in a tidal river using artificial neural networks. J Hydrol Eng 19(8):04014006

    Article  Google Scholar 

  • Iyer MS, Rhinehart RR (1999) A method to determine the required number of neural network training repetitions. IEEE Trans Neural Netw 10:427- –432

    Article  Google Scholar 

  • Khan MYA (2018) Spatial variation in the grain size characteristics of sediments in Ramganga River, Ganga Basin, India. In: Hussain CM (ed) Handbook of environmental materials management. Springer, Berlin

    Google Scholar 

  • Khan MYA, Chakrapani GJ (2016) Particle size characteristics of Ramganga catchment area of Ganga River. In: Geostatistical and geospatial approaches for the characterization of natural resources in the environment. Springer, Cham, pp 307–312

    Chapter  Google Scholar 

  • Khan MYA, Tian F (2018) Understanding the potential sources and environmental impacts of dissolved and suspended organic carbon in the diversified Ramganga River, Ganges Basin, India. Proc Int Assoc Hydrol Sci 379:61–66

    Google Scholar 

  • Khan MYA, Hasan F, Panwar S, Chakrapani GJ (2016a) Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India. Hydrol Sci J 61(11):2084–2095

    Article  Google Scholar 

  • Khan MYA, Gani KM, Chakrapani GJ (2016b) Assessment of surface water quality and its spatial variation. A case study of Ramganga River, Ganga Basin, India. Arab J Geosci 9(1):28

    Article  Google Scholar 

  • Khan MYA, Daityari S, Chakrapani GJ (2016c) Factors responsible for temporal and spatial variations in water and sediment discharge in Ramganga River, Ganga Basin, India. Environ Earth Sci 75(4):1–18

    Article  Google Scholar 

  • Khan MYA, Khan B, Chakrapani GJ (2016d) Assessment of spatial variations in water quality of Garra River at Shahjahanpur, Ganga Basin, India. Arab J Geosci 9(8):1–10

    Article  Google Scholar 

  • Khan MYA, Gani KM, Chakrapani GJ (2017) Spatial and temporal variations of physicochemical and heavy metal pollution in Ramganga River—a tributary of River Ganges, India. Environ Earth Sci 76(5):231

    Article  Google Scholar 

  • Khan MYA, Tian F, Hasan F, Chakrapani GJ (2018) Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga. International J Sediment Res, Ganges Basin, India. https://doi.org/10.1016/j.ijsrc.2018.09.001

    Book  Google Scholar 

  • Kisi O, Cigizoglu HK (2007) Comparison of different ANN techniques in river flow prediction. Civ Eng Environ Syst 24(3):211–231

    Article  Google Scholar 

  • Lee TL (2004) Back propagation neural network for long-term tidal prediction. Ocean Eng 31:225–238

    Article  Google Scholar 

  • Li X, Zecchin AC, Maier HR (2014) Selection of smoothing parameter estimators for general regression neural networks e Applications to hydrological and water resources modelling. Environ Model Softw 59:162–186

    Article  Google Scholar 

  • Liang SX, Li MC, Sun ZC (2008) Prediction models for tidal level including strong meteorological effects using neural networks. Ocean Eng 35:666–675

    Article  Google Scholar 

  • Lu W, Chu H, Zhang Z (2015) Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China. J Water Supply Res Technol-Aqua 64(1):95–104

    Article  Google Scholar 

  • Mehr AD, Kahya E, Şahin A, Nazemosadat MJ (2015) Successive-station monthly streamflow prediction using different artificial neural network algorithms. Int J Environ Sci Technol 12(7):2191–2200

    Article  Google Scholar 

  • Minns AW, Hall MJ (1996) Artificial neural networks as rainfall runoff models. Hydrol Sci J 41(3):399–417

    Article  Google Scholar 

  • Mishra A, Desai V, Singh V (2007) Drought forecasting using a hybrid stochastic and neural network model. J Hydrol Eng 12(6):626–638

    Article  Google Scholar 

  • Panwar S, Khan MYA, Chakrapani GJ (2016) Grain size characteristics and provenance determination of sediment and dissolved load of Alaknanda River, Garhwal Himalaya, India. Environ Earth Sci 75(2):91

    Article  Google Scholar 

  • Park YR, Murray TJ, Chung C (1996) Predicting sun spots using a layered perceptron neural network. IEEE Trans Neural Netw 7:501–505

    Article  Google Scholar 

  • Pektas AO, Cigizoglu HK (2017) Long-range forecasting of suspended sediment. Hydrol Sci J 62(14):2415–2425

    Article  Google Scholar 

  • Poggio T, Girosi F (1990) Regularization algorithms for learning that are equivalent to multilayer networks. Science 2247:978–982

    Article  Google Scholar 

  • Ray P (1998) Ecological imbalance of the Ganga river system: its impact on aquaculture. Daya Books, Delhi

    Google Scholar 

  • Rajurkar MP, Kothyari UC, Chaube UC (2002) Artificial neural networks for daily rainfall–runoff modelling. Hydrol Sci J 47(6):865–877

    Article  Google Scholar 

  • Sari V, dos Reis Castro NM, Pedrollo OC (2017) Estimate of suspended sediment concentration from monitored data of turbidity and water level using artificial neural networks. Water Resour Manag 1–15

  • Singh A, Imtiyaz M, Isaac RK, Denis DM (2014) Assessing the performance and uncertainty analysis of the SWAT and RBNN models for simulation of sediment yield in the Nagwa watershed, India. Hydrol Sci J 59(2):351–364

    Article  Google Scholar 

  • Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576

    Article  Google Scholar 

  • Strahler AN (1952) Dynamic basis of geomorphology. Geol Soc Am Bull 63(9):923–938

    Article  Google Scholar 

  • Sudheer KP, Jain SK (2003) Radial basis function neural network for modelling rating curves. J Hydrol Eng 8(3):161–164

    Article  Google Scholar 

  • Taurino AM, Distante C, Siciliano P, Vasanelli L (2003) Quantitative and qualitative analysis of VOCs mixtures by means of a microsensors array and different evaluation methods. Sens Actuators 93:117–125

    Article  Google Scholar 

  • Tokar AS, Markus M (2000) Precipitation-runoff Modelling using artificial neural networks and conceptual models. J Hydrol Eng 5(2):156–161

    Article  Google Scholar 

  • Tukoda S, Nagata K, Okada M (2013) A numerical analysis of learning coefficient in radial basis function network. IPSJ Trans Math Modell Appl 6(3):117–123

    Google Scholar 

  • Yingwei L, Sundararajan N, Saratchandaran P (1998) Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans Neural Netw 9:2

    Article  Google Scholar 

  • Yitian L, Gu RR (2003) Modeling flow and sediment transport in a river system using an artificial neural network. Environ Manage 31(1):122–134

    Article  Google Scholar 

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Acknowledgements

The author thankfully acknowledges the support provided by IIT Roorkee, India and Tsinghua University, Beijing, China. Author thanks the Council for Scientific and Industrial Research (CSIR), New Delhi, India for giving research fellowship. Central Water Commission, Lucknow, Government of India sympathetically gave the information important to the present work. The authors gratefully acknowledge the comments of the reviewers and the editor, which enormously improved the presentation of the manuscript.

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Authors and Affiliations

  1. Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Mohd Yawar Ali Khan

  2. Department of Mechanical Engineering, Z.H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, 202002, India

    Faisal Hasan

  3. Department of Hydraulic Engineering, School of Civil Engineering, Institute of Hydrology and Water Resources, Tsinghua University, Beijing, 100084, China

    Mohd Yawar Ali Khan & Fuqiang Tian

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  1. Mohd Yawar Ali Khan
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Correspondence to Mohd Yawar Ali Khan.

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Khan, M.Y.A., Hasan, F. & Tian, F. Estimation of suspended sediment load using three neural network algorithms in Ramganga River catchment of Ganga Basin, India. Sustain. Water Resour. Manag. 5, 1115–1131 (2019). https://doi.org/10.1007/s40899-018-0288-7

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