Abstract
Assessment of suspended sediments carried by streams and rivers is vital for planning and management of water resources structures and estimation of various hydrological parameters. More recently, soft computing techniques have been used in hydrological and environmental modeling. Adaptive Neuro-Fuzzy Inference System (ANFIS) is employed here to estimate sediment load at Indus River basin, India. Three different scenarios are considered to predict sediment load using ANFIS. Scenario one includes precipitation, temperature, and humidity as model input, but in case of scenario two, another one constraint infiltration loss is added with scenario one. Inclusion of evapotranspiration loss with scenario two forms scenario three that gives prominent value of performance. Mean square error (MAE) and coefficient determination (R2) are applied here to evaluate efficiency of model. Six different membership functions Pi, Trap, Tri, Gauss, Gauss2, and Gbell are applied for model development. In case if Gbell functions, scenario three shows best value of efficacy with R2 value 0.9811 and 0.9622 for training and testing phases, which is superior as compared to other two scenarios.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Buyukyildiz, M., Kumcu, S.Y.: An estimation of the suspended sediment load using adaptive network based fuzzy inference system, support vector machine and artificial neural network models. Water Resour. Manag. 31(4), 1343–1359 (2017)
Samantaray, S., Ghose, D.K.: Evaluation of suspended sediment concentration using descent neural networks. Procedia Comput. Sci. 132, 1824–1831 (2018a)
Samantaray, S., Ghose, D.K.: Evaluation of suspended sediment concentration using descent neural networks. Procedia comput Sci. 132, 1824–1831 (2018b)
Sahoo, A., Samantaray, S., Bankuru, S., Ghose, D.K.: Prediction of flood using adaptive neuro-fuzzy inference systems: a case study. In: Smart Intelligent Computing and Applications, pp. 733–739. Springer, Singapore (2020)
Rajaee, T., Mirbagheri, S.A., Zounemat-Kermani, M., Nourani, V.: Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Sci. Total Environ. 407(17), 4916–4927 (2009)
Ghose, D.K., Samantaray, S.: Modelling sediment concentration using back propagation neural network and regression coupled with genetic algorithm. Procedia Comput. Sci. 125, 85–92 (2018)
Ghose, D.K., Samantaray, S.: Sedimentation process and its assessment through integrated sensor networks and machine learning process. In: Computational Intelligence in Sensor Networks, pp. 473–488. Springer, Berlin, Heidelberg (2019)
Azamathulla, H.M., Cuan, Y.C., Ghani, A.A., Chang, C.K.: Suspended sediment load prediction of river systems: GEP approach. Arab. J. Geosci. 6(9), 3469–3480 (2013)
Yekta, A.H.A., Marsooli, R., Soltani, F.: Suspended sediment estimation of Ekbatan reservoir sub basin using adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANN), and sediment rating curves (SRC). In: Dittrich, Koll, Aberle, Geisenhainer (eds.) River Flow, pp. 807–813 (2010)
Adnan, R.M., Liang, Z., El-Shafie, A., Zounemat-Kermani, M., Kisi, O.: Prediction of suspended sediment load using data-driven models. Water 11(10), 2060 (2019)
Vafakhah, M.: Comparison of cokriging and adaptive neuro-fuzzy inference system models for suspended sediment load forecasting. Arab. J. Geosci. 6(8), 3003–3018 (2013)
Olyaie, E., Banejad, H., Chau, K.W., Melesse, A.M.: A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environ. Monit. Assess. 187(4), 189 (2015)
Samantaray, S., Sahoo, A., Ghose, D.K.: Assessment of runoff via precipitation using neural networks: watershed modelling for developing environment in arid region. Pertan. J. Sci. Technol. 27(4), 2245–2263 (2019)
Nivesh, S., Kumar, P.: River suspended sediment load prediction using neuro-fuzzy and statistical models: Vamsadhara river basin, India. World 2, 1 (2018)
Samantaray, S., Sahoo, A.: Estimation of runoff through BPNN and SVM in Agalpur watershed. In: Frontiers in Intelligent Computing: Theory and Applications, pp. 268–275. Springer, Singapore (2020)
Samantaray, S., Sahoo, A.: Appraisal of runoff through BPNN, RNN, and RBFN in Tentulikhunti watershed: a case study. In: Frontiers in Intelligent Computing: Theory and Applications, pp. 258–267. Springer, Singapore (2020)
Samantaray, S., Sahoo, A.: Assessment of sediment concentration through RBNN and SVM-FFA in Arid watershed, India. In: Smart Intelligent Computing and Applications, pp. 701–709. Springer, Singapore (2020)
Jang, J.S.R.: ANFIS adaptive–network-based-fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Brown, M., Harris, C.: Neuro-fuzzy Adaptive Modelling and Control. Prentice-Hall, Upper Saddle River, New Jersey (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mohanta, N.R., Biswal, P., Kumari, S.S., Samantaray, S., Sahoo, A. (2021). Estimation of Sediment Load Using Adaptive Neuro-Fuzzy Inference System at Indus River Basin, India. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_40
Download citation
DOI: https://doi.org/10.1007/978-981-15-5679-1_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5678-4
Online ISBN: 978-981-15-5679-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)