Modeling of streamflow- suspended sediment load relationship by adaptive neuro-fuzzy and artificial neural network approaches (Case study: Dalaki River, Iran)

Document Type: Research Paper

Authors

1 PhD Student, Faculty of Natural Resources, University of Tehran, Karaj, I.R. Iran

2 Associate Professor, Faculty of Natural Resources, University of Tehran, Tehran, Iran

3 MSc. Graduate, International Desert Research Center, University of Tehran, Tehran, Iran

Abstract

Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to its
essential application to water resources management. Recently, artificial intelligence has gained much popularity owing to
its application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. This
study made us of adaptive neuro-fuzzy inference system (ANFIS) techniques and three artificial neural network
approaches, namely, the Feed-forward back-propagation (FFBP), radial basis function-based neural networks (RBF),
geomorphology-based artificial neural network (GANN) to predict the streamflow suspended sediment relationship. To
illustrate their applicability and efficiency,, the daily streamflow and suspended sediment data of Dalaki River station in
south of Iran were used as a case study. The obtained results were compared with the sediment rating curve (SRC) and
regression model (RM). Statistic measures (RMSE, MAE, and R2) were used to evaluate the performance of the models.
From the results, adaptive neuro-fuzzy (ANFIS) approach combined capabilities of both Artificial Neural Networks and
Fuzzy Logic and then reflected more accurate predictions of the system. The results showed that accuracy of estimations
provided by ANFIS was higher than ANN approaches, regression model and sediment rating curve. Additionally, relating
selected geomorphologic parameters as the inputs of the ANN with rainfall depth and peak runoff rate enhanced the
accuracy of runoff rate, while sediment loss predictions from the watershed and GANN model performed better than the
other ANN approaches together witj regression equations in Modeling of stream flow–suspended sediment relationship.

Keywords


Agarwal, A., R. Singh, S. Mishra, P. Bhunya, 2005. ANNbased
sediment yield models for Vamsadhara river
basin (India). Water SA, 31(1); 95–100.
Agil, M., I. Kita, A. Yano, S. Nishiyama, 2007. Analysis
and prediction of flow from local source in a river basin
using a Neuro-fuzzy modeling tool. J Environ Manag,
85; 215–23.
Alp, M., H.K. Cigizoglu, 2007. Suspended sediment load
simulation by two artificial neural network methods 

22; 2–13.
Altun, H., A. Bilgil, B.C. Fidan, 2007. Treatment of multidimensional
data to enhance neural network estimators
in regression problems. Expert Syst Appl, 32(2); 599–
605.
ASCE Task Committee on the application of ANNs in
hydrology, 2000. Artificial neural networks in
hydrology, II: hydrologic application. J Hydrol Eng,
5(2); 124–37.
Bazoffi, P., G. Baldasarre, S. Vasca, 1996. Validation of
the PISA2 model for the automatic assessment of
reservoir sediment deposition. In Proceedings of the
International Conference on Reservoir Sediment
Deposition, Albertson M (ed.). Colorado State
University; 519–528.
Bhattacharya, B., R. Price, D. Solomatine, 2005. Datadriven
modelling in the context of sediment transport.
Phys Chem Earth, 30; 297–302.
Broomhead, D., D. Lowe, 1988. Multivariable functional
interpolation and adaptive networks. Complex Systems,
2; 321-355.
Brown, M., C. Harris, 1994. Neuro-fuzzy adaptive
modelling and control. Upper Saddle River, New
Jersey: Prentice-Hall.
Cigizoglu, H.K., 2005. Application of the generalized
regression neural networks to intermittent flow
forecasting and estimation. ASCE Journal of
Hydrologic Engineering, 10(4); 336e341.
Cigizoglu, H.K., M. Alp, 2006. Generalized regression
neural network in modelling river sediment yield. Adv
Eng Softw, 37; 63–8.
Dogan, A., H. Demirpence, M. Cobaner, 2008. Prediction
of groundwater levels from lake levels and climate data
using ann approach. Water SA, 34(2); 1–10.
Eberhart, R.C., R.W. Dobbins, 1990. Neural Network PC
Tools: A Practical Guide. Academic Press, San Diego,
414 pp.
El-Bakyr, M.Y., 2003. Feed forward neural networks
modeling for KeP interactions. Chaos, Solitions and
Fractals, 18(3); 995-1000 (Elsevier).
Engelund, F., E. Hansen, 1967. A monograph on sediment
transport in alluvial streams. Copenhagen: Danish
Technical (Teknisk Forlag).
Ferguson, R.I., 1986. River loads underestimated by rating
curves. Water Resour Res, 22; 74–6.
Hagan, M.T., M.B. Menhaj, 1994. Training feed forward
techniques with the Marquardt algorithm. IEEE
Transactions on Neural Networks, 5(6); 989-993.
Haykin, S., 1994. Neural Networks: a comprehensive
foundation. New York: MacMillan.
Hornik, K., M. Stinchcombe, H. White, 1989. Multilayer
feedforward networks are universal approximators.
Neural Netw, 2(5); 359–66.
Azamathulla, H.M., M.C. Deo, P.B. Deolalikar, 2008,
Alternative neural networks to estimate the scour below
spillways, Advances in Engineering Software, 39(8);
689-698.
Horowitz, A.J., 2008. Determining annual suspended
sediment and sediment-associated trace element and
nutrient fluxes. Sci Total Environ, 400; 315–43.
Jain, S.K., 2001. Development of integrated sediment
rating curves using Anns. J Hydraul Eng, 127(1); 30–7.
Jang, J.S.R., 1993. ANFIS: adaptive-network-based fuzzy
inference system. IEEE Trans. Sys. Manage and
Cybernetics, 23(3); 665–685.
Jang, J.S.R., C.T. Sun, 1995. Neuro-fuzzy modelling and
control. Proc IEEE, 83; 378–406.
Jang, J.S.R., C.T. Sun, E. Mizutani, 1997. Neuro-fuzzy and
soft computing: a computational approach to learning
and machine intelligence. Upper Saddle River, New
Jersey, USA: Prentice Hall.
Kim, B., S.E. Lee, M.Y. Song, J.H. Choi, S.M. Ahn, K.S.
Lee, et al, 2008. Implementation of artificial neural
networks (ANNs) to analysis of inter-taxa communities
of benthic microorganisms and macroinvertebrates in a
polluted stream. Sci Total Environ, 390; 262–74.
Kisi, O., 2004a. River flow modeling using artificial neural
networks. Journal of Hydrologic Engineering, ASCE
9(1); 60–63.
Kisi, O., 2004b. Multi-layer perceptrons with Levenberg–
Marquardt optimization algorithm for suspended
sediment concentration prediction and estimation.
Hydrological Sciences Journal, 49(6); 1025–1040.
Kisi, O., 2005. Suspended sediment estimation using
neuro-fuzzy and neural network approaches.
Hydrological Sciences Journal, 50(4); 683–696.
Kisi, O., E. Karahan, Z. Sen, 2006. River suspended
sediment modelling using a fuzzy logic approach.
Hydrol Process, 20(20); 4351–4362.
Kisi, O., T. Haktanir, M. Ardiclioglu, O. Ozturk, E. Yalcin,
S. Uludag, 2008. Adaptive neuro-fuzzy computing
technique for suspended sediment estimation. Adv Eng
Softw, 40; 438–444.
Legates, D.R., G.J. McCabe Jr, 1999. Evaluating the use of
goodness-of-fit measures in hydrologic and
hydroclimatic model validation. Water Resour Res,
35(1); 233–241.
Tuan, L.T., T. Shibayama, 2003. Application of GIS to
Evaluate Long-Term Variation of Sediment due to
Coastal Environment, Coastal Engineering Journal,
JSCE, 45(2); 275-293.
Lohani, A.K., N.K. Goel, K.K. Bhatia, 2007. Deriving
stage–discharge–sediment concentration relationships
using fuzzy logic. Hydrol Sci J, 52(4); 793–807.
Masters, T., 1993. Practical neural network recipes in C++.
San Diego (CA): Academic Press.
Mirbagheri, S.A., K.K. Tanji, R.B. Krone, 1988a.
Sediment characterization and transport in Colusa Basin
Drain. J Environ Eng, 114(6); 1257–73.
Mirbagheri, S.A., K.K. Tanji, R.B. Krone, 1988b.
Simulation of suspended sediment in Colusa Basin
Drain. J Environ Eng, 114(6); 1274–93.
Nagy, H.M., K. Watanabe, M. Hirano, 2002. Prediction of
load concentration in rivers using artificial neural
network model. J Hydraul Eng, 128(6); 588–95.
Nash, J.E., J.V. Sutcliffe, 1970. River flow forecasting
through conceptual models part I — a discussion of
principles. J Hydrol, 10(3); 282–90.
Nayak, P.C., K.P. Sudheer, D.M. Rangan, K.S. Ramasastri,
2004. A neuro-fuzzy computing technique for modeling
hydrological time series. Journal of Hydrology, 291(1–
2); 52–66.

Nourani, V., A.A. Mogaddam, A.O. Nadiri, 2008. An
ANN-based model for spatiotemporal groundwater
level forecasting. Hydrol Process, 22; 5054–5066.
Nourani, V., M.T. Alami, M.H. Aminfar, 2009. A
combined neural-wavelet model for prediction of
Ligvanchai watershed precipitation. Eng Appl Artif
Intell, 22; 466–472.
Nourani, V., M. Komasi, A. Mano, in press. A multivariate
ANN-wavelet approach for rainfall-runoff modeling.
Water Resources Management, Published online,
doi:10.1007/s11269-009-9414-5.
Ocampo-Duque, W., M. Schuhmacher, J.L. Domingo,
2007. A neural-fuzzy approach to classify the ecological
status in surface waters. Environ Pollut, 148; 634–641.
Poggio, T., F. Girosi, 1990. Regularization algorithms for
learning that are equivalent to multilayer networks.
Science, 2247; 978-982.
Raghuwanshi, N., R. Singh, L. Reddy, 2006. Runoff and
sediment yield modeling using artificial neural
networks: Upper Siwane River, India. J Hydrol Eng,
11(1); 71–79.
Rajaee, T., S.A. Mirbagheri, M. Zounemat-Kermani, V.
Nourani, 2009. Daily suspended sediment concentration
simulation using ANN and neuro-fuzzy models. Science
of the Total Environment, 407; 4916-4927.
Rajaee, T., V. Nourani, M. Zounemat-Kermani, K. Ozgur,
2011. River Suspended Sediment Load Prediction:
Application of ANN and Wavelet Conjunction Model .
Journal of Hydrologic Engineering, ASCE, 16(8); 613-
627.
Raman, H., N. Sunilkumar, 1995. Multivariate modelling
of water resources time series using artificial neural
networks. Hydrol Sci J, 40(2); 145–63.
Restrepo, J.D., J.P.M. Syvitski, 2006. Assessing the Effect
of Natural Controls and Land Use Chane on Sediment
Yield in a Major Andean River: The Magdalena
Drainage Basin, Colombia. Ambio: a Journal of the
Human Environment, 35; 44-53.
Sahoo, G.B., C. Ray, E. Mehnert, D.A. Keefer, 2006.
Application of artificial neural networks to assess
pesticide contamination in shallow groundwater. Sci
Total Environ, 367; 234–51.
Salas, J.D., J.W. Delleur, V. Yevjevich, W.L. Lane, 1980.
Applied modeling of hydrological time series. Denver:
Water Resources Publications.
Sarangi, A., A.K. Bhattacharya, 2005. Comparison of
Artificial Neural Network and regression models for
sediment loss prediction from Banha watershed in India.
Agricultural Water Management, 78; 195–208.
Sayed, T., A. Tavakolie, A. Razavi, 2003. Comparison of
adaptive network based fuzzy inference systems and Bspline
neuro-fuzzy mode choice models. Water
Resources Research, 17(2); 123–130.
Schuller, B., 1999. Automatisches Verstehen gesprochener
mathematischer Formeln. Diploma thesis, Technische
Universit¨at M¨unchen, Munich, Germany.
Sha, W., 2007. Comment on: ‘flow forecasting for a
Hawaii stream using rating curves and neural
networks’ by G.B. Sahoo and C. Ray. Journal of
Hydrology 340 (1–2), 119–121. Journal of Hydrology,
317; 63–80.
Sinnakaudan S.K., A.A.B. Ghani, M.S.S, Ahmad, N.A.
Zakaria, 2006. Multiple linear regression model for total
bed material load prediction. Journal of Hydraulic
Engineering, 132(5); 521-528.
Tahmoures, M., A. Karimi, 2008. Estimation of Daily
Suspended Sediment Yield Based on Neural Networks
and Neuro-Fuzzy Technique, Pajouhesh-va-sazandegi
Journal, 21; 61–75.
Taurino, A.M., C. Distante, P. Siciliano, L. Vasanelli,
2003. Quantitative and qualitative analysis of VOCs
mixtures by means of a microsensors array and different
evaluation methods. Sensors and Actuators, 93; 117-
125.
Tay, J.H., X. Zhang, 1999. Neural fuzzy modeling of
anaerobic biological wastewater treatment systems.
J.Environ, 125(12); 1149-1159.
Tayfur, G., S. Ozdemir, V.P. Singh, 2003. Fuzzy logic
algorithm for runoff-induced sediment transport from
bare soil surfaces. Adv Water Resour, 26; 1249–1256.
Tokar, A.S., P.A. Johnson, 1999. Rainfall runoff modelling
using artificial neural networks. J Hydrol Eng, 4(3);
232–239.
Tsai, C.H., L.C. Chang, H.C. Chiang, 2009. Forecasting of
ozone episode days by cost-sensitive neural network
methods. Sci Total Environ, 407; 2124–35.
Vanacker, V., M. Vanderschaeghe, G. Govers, E. Willems,
J. Poesen, J. Deckers, B. De Biévre, 2009. Linking
hydrological, infinite slope stability and land use change
models through GIS for assessing the impact of
deforestation on landslide susceptibility in high Andean
watersheds. Geomorphology, 52; 299-315.
Verstraeten, G., J. Poesen, 2001. Factors controlling
sediment yield fromsmall intensively cultivated
catchments in a temperate humid climate.
Geomorphology, 40; 123–44.
Williams, G.P., 1989. Sediment concentration versus water
discharge during single hydrologic events in rivers. J
Hydrol, 111(1–4); 89–106.
Yang, C.T., 1996. Sediment transport, theory and practice.
New York: McGraw-Hill.
Zhu, Y.M., X.X. Lu, Y. Zhou, 2007. Suspended sediment
flux modeling with artificial neural network: an
example of the Longchuanjiang River in the Upper
Yangtze Catchment, China. Geomorphology, 84; 111–
25.
Zounemat-Kermani, M., M. Teshnehlab, 2008. Using
adaptive neuro-fuzzy inference system for hydrological
time series prediction. Appl Soft Comput, 8; 928–936.
Zounemat-Kermani, M., A.A. Beheshti, B. Ataie-Ashtiani,
S.R. Sabbagh-Yazdi, 2009. Estimation of currentinduced
scour depth around pile groups using neural
network and adaptive neuro-fuzzy inference system.
Appl Soft Comput, 9; 746–55.