Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data

Journal of Hydrology - Tập 367 - Trang 52-61 - 2009
M. Cobaner1, B. Unal2, O. Kisi1
1Civil Engineering Department, Erciyes University, Kayseri 38039, Turkey
2Civil Engineering Department, Bozok University, Yozgat 66100, Turkey

Tài liệu tham khảo

Alp, 2007, Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data, Environmental Modelling and Software, 22, 2, 10.1016/j.envsoft.2005.09.009 ASCE Task Committee, 2000, Artificial neural networks in hydrology. II: Hydrological applications, Journal of Hydrologic Engineering, ASCE, 5, 124, 10.1061/(ASCE)1084-0699(2000)5:2(124) Bae, 2007, Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique, Hydrological Sciences Journal, 52, 99, 10.1623/hysj.52.1.99 Chang, 2001, A counterpropagation fuzzy-neural network modeling approach to real time stream flow prediction, Journal of Hydrology, 245, 153, 10.1016/S0022-1694(01)00350-X Cigizoglu, 2004, Estimation and forecasting of daily suspended sediment data by multi layer perceptrons, Advances in Water Resources, 27, 185, 10.1016/j.advwatres.2003.10.003 Cigizoglu, 2005, Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data, Nordic Hydrology, 36, 49, 10.2166/nh.2005.0005 Cigizoglu, 2006, Methods to improve the neural network performance in suspended sediment estimation, Journal of Hydrology, 317, 221, 10.1016/j.jhydrol.2005.05.019 Cybenco, 1989, Approximation by superposition of a sigmoidal function, Mathematics of Control, Signals and Systems, 2, 303, 10.1007/BF02551274 Dogan, 2008, Prediction of groundwater levels from lake levels and climate data using ann approach, Water SA, 34, 1 El-Bakyr, 2003, Feed forward neural networks modelling for K–P interactions, Chaos, Solitons & Fractals, 18, 995, 10.1016/S0960-0779(03)00068-7 Fausett, 1994 Ferguson, 1986, River loads underestimated by rating curves, Water Resources Research, 22, 74, 10.1029/WR022i001p00074 Giustolisi, 2005, Improving generalization of artificial neural networks in rainfall–runoff modeling, Hydrological Sciences Journal, 50, 439, 10.1623/hysj.50.3.439.65025 Goh, 1995, Back-propagation neural networks for modeling complex systems, Artificial Intelligence in Engineering, 9, 143, 10.1016/0954-1810(94)00011-S Hagan, 1994, Training feed forward networks with the Marquardt algorithm, IEEE Trans Neural Networks, 6, 861 Haykin, 1998 Hornik, 1989, Multilayer feed forward networks are universal approximators, Neural Networks, 2, 359, 10.1016/0893-6080(89)90020-8 Jain, 2001, Development of integrated sediment rating curves using ANNs, Journal of Hydraulic Engineering, ASCE, 127, 30, 10.1061/(ASCE)0733-9429(2001)127:1(30) Jain, 1999, Application of ANN for reservoir inflow prediction and operation, Journal of Water Resources Planning and Management, ASCE, 125, 263, 10.1061/(ASCE)0733-9496(1999)125:5(263) Jang, 1993, ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics, 23, 665, 10.1109/21.256541 Jayawardena, 2006, Determining the structure of a radial basis function network for prediction of nonlinear hydrological time series, Hydrological Sciences Journal, 51, 21, 10.1623/hysj.51.1.21 Karunanithi, 1994, Neural networks for river flow prediction, Journal of Computing in Civil Engineering, ASCE, 8, 201, 10.1061/(ASCE)0887-3801(1994)8:2(201) Kaya, M.D., Haşiloğlu, A. S., Yeşilyurt, H., 2002. To estimate the design of functional sizes of chairs and desks on the basis of ISO 5970 using adaptive neuro-fuzzy inference system. In: FSSCIMIE’02, May 29–31. Kisi, O., 2003. Modelling of suspended sediment yield in a river cross-section using fuzzy logic. Ph.D. Thesis, Istanbul Technical University Institute of Science and Technology, Istanbul, Turkey. Kisi, 2004, River flow modeling using artificial neural networks, Journal of Hydrologic Engineering, ASCE, 9, 60, 10.1061/(ASCE)1084-0699(2004)9:1(60) Kisi, 2004, Multi-layer perceptrons with Levenberg–Marquardt optimization algorithm for suspended sediment concentration prediction and estimation, Hydrological Sciences Journal, 49, 1025, 10.1623/hysj.49.6.1025.55720 Kisi, 2004, Daily suspended sediment modelling using a fuzzy differential evolution approach, Hydrological Sciences Journal, 49, 183, 10.1623/hysj.49.1.183.54001 Kisi, 2005, Daily river flow forecasting using artificial neural networks and auto-regressive models, Turkish Journal of Engineering and Environmental Sciences, 29, 9 Kisi, 2005, Suspended sediment estimation using neuro-fuzzy and neural network approaches, Hydrological Sciences Journal, 50, 683, 10.1623/hysj.2005.50.4.683 Kisi, 2006, River suspended sediment modeling using fuzzy logic approach, Hydrological Processes, 20, 4351, 10.1002/hyp.6166 Lee, 2003, Radial basis function networks applied to DNBR calculation in digital core protection systems, Annals of Nuclear Energy, 30, 1561, 10.1016/S0306-4549(03)00099-9 Leonard, 1992, Using radial basis functions to approximate a function and its error bounds, IEEE Transactions on Neural Networks, 3, 624, 10.1109/72.143377 Lohani, 2007, Deriving stage–discharge–sediment concentration relationships using fuzzy logic, Hydrological Sciences Journal, 52, 793, 10.1623/hysj.52.4.793 Loukas, 2000, Radial basis function networks in host–guest interactions: instant and accurate formation constant calculations, Analytica Chimica Acta, 417, 221, 10.1016/S0003-2670(00)00934-X Lu, C., De Brabanter, J., Van Huffel, S., Vergote, I., Timmerman, D., 2001. Using artificial neural networks to predict malignancy of ovarian tumors. In: 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Marquardt, 1963, An algorithm for least squares estimation of non-linear parameters, Journal of the Society for Industrial and Applied Mathematics, 11, 431, 10.1137/0111030 Masters, 1993 McBean, 1988, Uncertainty in suspended sediment transport curves, Journal of Hydrologic Engineering, ASCE, 114, 63, 10.1061/(ASCE)0733-9429(1988)114:1(63) Sandy, 1990 Sayed, 2003, Comparison of adaptive network based fuzzy inference systems and B-spline neuro-fuzzy mode choice models, Water Resources Research, 17, 123 Sha, 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, 119, 10.1016/j.jhydrol.2007.04.003 Takagi, 1985, Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man and Cybernetics, 15, 116, 10.1109/TSMC.1985.6313399 Tayfur, 2002, Artificial neural networks for sheet sediment transport, Hydrological Sciences Journal, 47, 879, 10.1080/02626660209492997 Tayfur, 2006, Artificial neural networks for estimating daily total suspended sediment in natural streams, Nordic Hydrology, 37, 69, 10.2166/nh.2006.0006 Tayfur, 2003, Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces, Advances in Water Resources, 26, 1249, 10.1016/j.advwatres.2003.08.005 Tokar, 1999, Rainfall–runoff modelling using artificial neural networks, Journal of Hydrologic Engineering, ASCE, 4, 232, 10.1061/(ASCE)1084-0699(1999)4:3(232) Wagener, 2004 Wasserman, 1993