River suspended sediment load prediction using neuro-fuzzy and statistical models: Vamsadhara river basin, India
DOI:
https://doi.org/10.53550/Keywords:
Adaptive neuro-fuzzy inference system, Multiple linear regression, Sediment rating curve, Training and testingAbstract
The present study deals with the development of Adaptive Neuro-Fuzzy Inference System (ANFIS), Multiple Linear Regression (MLR) and Sediment Rating Curve (SRC) models to estimate the suspended sediment load from Vamsadhara River basin comprising of 7820 km2 area situated between Mahanadi and Godavari river basins. Three different inputs or cases using ANFIS, MLR and SRC were employed to find the effect of different inputs on the suspended sediment load. The developed models were trained and tested. Three statistical parameters: Root Mean Square Error (RMSE), coefficient of determination (R2) and Coefficient of Efficiency (CE) were used to compare the results of the models. Results revealed that the neuro-fuzzy model (RMSE = 44.02 kg sec-1, R2 = 0.99 and CE = 99.06%) is in good agreement with the observed values and present better performance in suspended sediment load prediction in comparison to the traditional models like MLR (RMSE = 188.28 kg sec-1, R2 = 0.828 and CE = 82.82%) and SRC (RMSE = 331.69 kg sec-1, R2 = 0.617 and CE = 56.48%).
References
Ardiclioglu, M., Kisi, O. and Haktanir, T. 2007. Suspended sediment prediction by using two different feed-forward back propagation algorithms. Can. J. Civ. Eng., 34(1): 120-125.
Bae, D.H., Jeong, D.M. and Kim, G. 2007. Monthly dam inflow forecasts using weather forecasting information and neurofuzzy technique. Hydrolog. Sci. J., 52(1): 99-113.
Bilgili, M. 2010. Prediction of soil temperature using regression and artificial neural network models. Meteorol. Atmos. Phys., 110: 59-70.
Brevik, E.C., Cerda, A., Mataix-Solera, J., Pereg, L., Quinto, J.N., Six, J. and Van Oost, K. 2015. The interdisciplinary nature of Soil. Soil, 1: 117-129.
Chang, C.K, Ghani, A.A., Abdullah, R. and Zakaria, N.A. 2008. Sediment transport modelling for Kulim River case study. J. Hydro. Environ. Res., 2(1): 47-59.
Chang, F.J. and Chen, Y.C. 2001. A counter propagation fuzzyneural network modelling approaches to real time streamflow prediction. J. Hydrol., 245: 153-164.
Chang, F.J., Hu, H.F. and Chen, Y.C. 2001. Counter propagation fuzzy-neural network for streamflow reconstruction. Hydrol. Process., 15(2): 219-323.
Cigizoglu, H.K. and Kisi, O. 2006. Methods to improve the neuralnetwork performance in suspended sediment estimation. J. Hydrol., 317: 221-238.
Cigizoglu, H.K. 2003. Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrolog. Sci. J., 48(3): 349-361.
Decock. C., Lee, J., Necpalova, M., Pereira, E.I.P., Tendall, D.M. and Six, J. 2015. Mitigating N O emissions from soil: from patching 2 leaks to transformative action. Soil, 1: 687-694.
Engeland, K. and Hisdal, H. 2009. A comparison of low flow estimates in ungauged catchments using regional regression and the HBV-model. Water Resour. Manag., 23(12): 2567-2586.
Eslamian, S.S, Ghasemizadeh, M., Biabanaki, M. and Talebizadeh, M. 2010. A principal component regression method for estimating low flow index. Water Resour. Manag., 24(11): 2553-2566.
Giustolisi, O. and Lauchelli, D. 2005. Improving generalization of artificial neural networks in rainfall-runoff modelling. Hydrolog. Sci. J., 50(3): 515-528.
Gregory, K.J. and Walling, D.E. 1973. Drainage basin form and processes: A Geomorphological Approach. Halsted (Wiley), New York, 456 p.
Holeman, J.N. 1968. The Sediment Yield of Major Rivers of the World. Water Resour. Res., 4: 4.
Hundecha, Y., Bardossy, A. and Theissen, H.W. 2001. Development of a fuzzy logic-based rainfall-runoff model. Hydrolog. Sci. J., 46(3): 363-376. ICAR and NAAS. 2010.
Degraded and Wastelands of India: Status and spatial distribution. Indian Council of Agricultural Research, New Delhi, India, 158 p. Jang, J.S.R. and Sun, C.T. 1995. Neuro-fuzzy modeling and control. Proc. IEEE, 83: 378-406.
Jayawardena, A.W., Xu, P.C., Tsang, F. and Li, W.K. 2006. Determining the structure of a radial basis function network for prediction of nonlinear hydrological time-series. Hydrolog. Sci. J., 51(1): 21-44.
Kisi, O. and Cobaner, M., 2009. Modelling river-stage discharge relationship using different neural networks. CLEAN-Soil, Air, Water, 37(2): 160-169.
Kisi, O. 2007. River flow forecasting using different artificial neural network algorithms. J. Hydrol. Eng., 12(5): 532-539.
Kisi, O. 2004. River flow modelling using artificial neural networks. J. Hydrol. Eng., 9(1): 60-63.
Kisi, O. 2008. River flow forecasting and estimation using different artificial neural network techniques. Hydrol. Res., 39(1): 27-40.
Kisi, O. 2016. A new approach for modelling suspended sediment: Evolutionary fuzzy approach. Hydrol. Earth Syst. Sci. Discuss., doi.org/10.5194/hess-2016-213, 2016.
Keesstra, S.D., Bouma, J., Wallinga, J., TIttonell, P., Smith, P., Cerda, A., Luca, M., Quinton, J., Pachepsky, P., Van der putten, W.H., Bardgett, R.D., Moolenaar, S., Mol, G., Jansen, B. and Fresco,
O.L., 2016. The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals. Soil, 2(2): 111-128.
Lohani, A.K, Goel, N.K. and Bhatia, K.K.S. 2006. Takagi-Sugeno fuzzy inference system for modelling stage-discharge relationship. J. Hydrol., 331(1-2): 146-160.
Marofi, S., Tabari, H. and Zare, A.H. 2011. Predicting spatial distribution of snow water equivalent using multivariate nonlinear regression and computational intelligence methods. Water Resour. Manage., 25: 1417-1435.
Naik, B.S., Paul, J.C., Panigrahi, B. and Sahoo, B.C. 2014. Soil loss from agricultural lands in eastern ghat of Odisha - A case study of Koraput district. J. Soil Water Cons., 13(4): 324-329.
Naik, B.S., Paul, J.C., Panigrahi, B. and Sahoo, B.C. 2015. Soil erosion assessment from farming lands of Koraput district in eastern ghat region of Odisha. Indian J. Soil Cons., 43(1): 33-37.
Nayak, P.C., Sudheer, K.P. and Ramasastri, K.S. 2004. A neurofuzzy computing technique for modelling hydrological time series. J. Hydrol., 291: 52-66.
Nayak, P.C., Sudheer, K.P. and Ramasastri, K.S. 2005a. Fuzzy computing based rainfall-runoff model for real time flood forecasting. Hydrol. Process., 19: 955-968.
Nayak, P.C., Sudheer, K.P. and Ramasastri, K.S. 2005b. Short-term flood forecasting with a neuro-fuzzy model. Water Resour. Manag., 41: 2517-2530.
Oinam, B.C., Marx, W., Scholten, T. and Wieprecht, S. 2014. Fuzzy
rule base approach for developing soil a protection index map: a case study in the upper Awash basin, Ethopian highlands. Land Degrad. Dev., 25(5): 483-500.
Sadeghi, S.H.R., Seghaleh, M.B. and Rangaver, A.S. 2013. Plot sizes dependency of runoff and sediment yield estimates from small watersheds. Catena, 102: 55-61.
Sahoo, N., Pradhan, P.C. and Panigrahi, B. 1994. Erosion and flow resistance characteristics of overland flow in catchments with cohesive sediments. J. Indian Water Resour. Soc., 14 (1&4): 73-80.
See, L. and Openshaw, S. 2000. Applying soft computing approaches to river level forecasting. Hydrol. Sci. J., 44(5): 763-779.
Stuber, M., Gemmar, P. and Greving, M. 2000. Machine supported development of fuzzy-flood forecast systems. In: European Conf. on Adv. In Flood Res. (Potsdam, Germany) (ed. By A. Bronstert, C. Bismuth and L. Menzel), pp 504-515.
Tabari, H., Marofi, S., Zare, A.H. and Sharifi, M.R. 2010. Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in samsami basin of Iran. Neural Comput. Appl., 19: 625-635.
Tallis, J.H. 1998. Growth and Degradation of British and Irish Blanket mires. Environ. Rev., 6(2): 81-122.
Takagi, T. and Sugeno, M.1985. Fuzzy identification of systems and its applications to modelling and control. IEEE Trans. Syst. Man. Cybernet., 15: 116-132.
Tayfur, G. 2002. Artificial neural networks for sheet sediment transport. Hydrol. Sci. J., 47(6): 879-892.
Tayfur, G. and Guldal, V. 2006. Artificial neural networks for daily total suspended sediment in natural streams. Hydrol. Res., 37(1): 69-79.
Vercruysse, K., Robert, C.G. and Rickson, R.J. 2017. Suspended sediment transport dynamics in rivers: Multi-scale driver of temporal variation. Earth Sci. Rev., 166: 38-52.
Verstraeten, G. and Poesen, J. 2001. Factors controlling sediment transport from small intensively cultivated catchment in a temperate humid climate. Geomorphology, 40(1-2): 123-144.
Wang, P. and Linker, L.C. 2008. Improvement of regression simulation in fluvial sediment loads. J. Hydrol. Eng., 134: 1527-1531.
Wang, J., Ge, J., Hu, Y., Li, C. and Wang, L. 2015. Fuzzy intelligence system for land consolidation-a case study for Shunde, China. Soil Earth, 6: 997-1006.
White, S. 2005. Sediment yield prediction and modelling. Hydrolog. Process., 19: 3053-3057.
Xiong, L.H., Shamsaldein, A.Y. and O`Connor, K.M. 2001. A nonlinear combination of the forecasts of rainfall-runoff models by the first order Takagi-Sugeno fuzzy system. J. Hydrol., 245: 196-217.
Yang, C.T. 1996. Sediment transport theory and practice, McGraw-Hill, New York.
Yurdusev, M.A., Firat, M. and Turan, M.E., 2009. Adaptive neuro fuzzy inference system approach for municipal water consumption modelling: an application to Izmir, Turkey. J. Hydrol., 365 (3-4): 225-234.
Zhu, M.L, Fujita, M., Hashimoto, N. and Kudo, M. 1994. Long lead time forecast of runoff using fuzzy reasoning method. J. Japan. Soc. Hydrol. Water Resour., 7(2): 83-89.
Zounemat-Kermani, M. and Teshnehlab, M. 2008. Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Appl. Soft Comput., 8: 928-936.
