Abstract
Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. Also, the average value of the AI of MSRCs is 1.15 which is an acceptable amount of the other considered various methods.
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References
Abrishamchi A, Ebrahimian A, Tajrishi M, Mariko MA (2005) Case study: application of multicriteria decision making to urban water supply. J Water Resour Plann Manage 131(4):326–335
Alizadeh MJ, Jafari Nodoushan E, Kalarestaghi N, Chau KW (2017) Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models. Environ Sci Pollut Res 24(36):28017–28025. https://doi.org/10.1007/s11356-017-0405-4
Alp M, Kerem Cigizoglu H (2007) Suspended sediment load simulation by two artificial neural network methods using hydro-meteorological data. Environ Model Softw 22(1):2–13. https://doi.org/10.1016/j.envsoft.2005.09.009
Arabkhedri M, Varvani J, Hakimkhani SH (2004) The validity of extrapolation methods in estimation of annual mean suspended sediment yield (17 hydrometric stations). J Agric Sci Nat Resour 13:123–131
Asselman NEM (2000) Fitting and interpretation of sediment rating curves. J Hydrol 234(3–4):228–248. https://doi.org/10.1016/S0022-1694(00)00253-5
Bialik RJ, Czernuszenko W (2013) On the numerical analysis of bed-load transport of saltating grains. Int J Sediment Res 28:413–420
Bialik RJ, Nikora VI, Rowinski PM (2012) 3D Lagrangian modelling of saltating particles diffusion in turbulent water flow. Acta Geophys 60(6):1639–1660. https://doi.org/10.2478/s11600-012-0003-2
Bialik RJ, Karpiński M, Rajwa A, Luks B, Rowiński PM (2014) Bedform characteristics in natural and regulated channels: a comparative field study on the Wilga River, Poland. Acta Geophys 62(6):1413–1434. https://doi.org/10.2478/s11600-014-0239-0
Boning WC (2001) Recommendations for use of retransformation methods in regression, models used to estimate sediment loads. http://water.Usgs.Gov
Chen XY, Chau KW (2016) A hybrid double feedforward neural network for suspended sediment load estimation. Water Resour Manage 30(7):2179–2194. https://doi.org/10.1007/s11269-016-1281-2
Chiang YM, Chang LC, Tsai MJ, Wang YF, Chang FJ (2011) Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks. Hydrol Earth Syst Sci 15:185–196. https://doi.org/10.5194/hess-15-185-2011
Cohn TA, Delong LL, Gilroy EJ, Hirsch RM, Wells DK (1989) Estimating constituent loads. Water Resour Res 25(5):937–942
Cohn AT, Dana LC, Edward JG, Linda DZ, Robert MS (1992) The validity of a simple statistical model for estimating fluvial constituent loads: an empirical study involving nutrient loads entering the Chesapeake Bay. Water Resour Res 28(9):937–942
Degens BP, Donohue RD (2002) Sampling mass loads in rivers: a review of approaches for identifying, evaluating and minimizing estimation errors. Water Resour Tech Ser 1–43
Fan X, Shi C, Zhou Y, Shao W (2012) SRCs in the Ningxia-Inner Mongolia reaches of the upper Yellow River and their implications. Quat Int 282:152–162. https://doi.org/10.1016/j.quaint.2012.04.044
Ferguson RI (1986) River loads underestimated by rating curves. Water Resour Res 22:74–76. https://doi.org/10.1029/WR022i001p00074
Ferguson RI (1987) Accuracy and precision of methods for estimating river loads. Earth Surf Process Land Forms 12:95–104. https://doi.org/10.1002/esp.3290120111
Firat M (2008) Comparison of artificial intelligence techniques for river flow forecasting. Hydrol Earth Syst Sci 12:123–139. https://doi.org/10.5194/hess-12-123-2008
Gholami V (2013) The influence of deforestation on runoff generation and soil erosion (case study: Kasilian Watershed). J For Sci 59(7):272–278
Gholami V, Khaleghi MR, Sebghati M (2017) A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS). Appl Water Sci 7(7):3633–3647
Gholami V, Booij MJ, Tehran EN, Hadian MA (2018) Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data. CATENA 163:210–218
Gholzom EH, Gholami V (2012) A comparison between natural forests and reforested lands in terms of runoff generation potential and hydrologic response (case study: Kasilian watershed). Soil Water Res 7(4):166–173
Holtschlag DJ (2001) Optimal estimation of suspended-sediment concentrations in streams. Hydrol Process 15:1133–1156. https://doi.org/10.1002/hyp.207
Horowitz AJ (2003) An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations. Hydrol Process 17:387–3409. https://doi.org/10.1002/hyp.1299
Iadanza C, Napolitano F (2006) Sediment transport time series in the Tiber River. Phys Chem Earth Parts A/B/C 31(18):1212–1227. https://doi.org/10.1016/j.pce.2006.05.005
Jain S (2001) Development of integrated sediment rating curves using ANNs. J Hydraul Eng 127(1):30–37. https://doi.org/10.1061/(ASCE)0733-9429(2001)127:1(30)
Jang JSR (1993) ANFIS – Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Jansson MB (1996) Estimating a sediment rating curves of the Reventon river at Palomo using logged mean loads within discharge classes. J Hydrol 183(4):227–241
Jansson MB (1997) Comparison of sediment rating curves developed on load and on concentration. Nord Hydrol 28(3):189–200. https://doi.org/10.2166/nh.1997.011
Julien B (1994) Water quality management with imprecise information. Eur J Oper Res 76:15–27
Khaleghi MR, Varvani J (2018a) Simulation of the relationship between river discharge and sediment yield in the semi-arid river watersheds. Acta Geophys 66:109–119. https://doi.org/10.1007/s11600-018-0110-9
Khaleghi MR, Varvani J (2018b) Sediment rating curve parameters relationship with watershed, characteristics in the semiarid river watersheds. J Arab J Sci Eng. https://doi.org/10.1007/s13369-018-3092-7
Khaleghi MR, Gholami V, Ghodusi J, Hosseini H (2011) Efficiency of the geomorphologic instantaneous unit hydrograph method in flood hydrograph simulation. CATENA 87:163–171. https://doi.org/10.1016/j.catena.2011.04.005
Khaleghi MR, Ghodusi J, Ahmadi H (2014) Regional analysis using the geomorphologic instantaneous unit hydrograph (GIUH) method. Soil Water Res 9(1):25–30. https://doi.org/10.17221/33/2012-SWR
Koch RW, Smillie GM (1986) Comment on “River loads underestimated by rating curves” by R. I. Ferguson. Water Resour Res 22(13):2121–2122
Liu B (2000) Dependent-chance programming in fuzzy environments. Fuzzy Sets Syst 109:97–106
Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1–2):52–66
Olyaie E, Banejad H, Chau KW, Melesse AM (2015) A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in the United States. Environ Monit Assess 187(4):189. https://doi.org/10.1007/s10661-015-4381-1
Peng H, Zhou H (2011) A fuzzy-dependent chance multi-objective programming for water resources planning of a coastal city under fuzzy environment. Water Environ J 25:40–54. https://doi.org/10.1111/j.1747-6593.2009.00187.x
Phillips JM, Webb BW, Walling DE, Leeks GJL (1999) Estimating the suspended sediment loads of rivers in the LOIS study area using infrequent samples. Hydrol Process 13:1035–1050. https://doi.org/10.1002/(SICI)1099-1085(199905)
Preston SV, Bierman J (1989) An evaluation of methods for the estimation of tributary mass loads. Water Resour Res 25(6):1379–1390. https://doi.org/10.1029/WR025i006p01379
Schluter M, Savitsky AG, McKinney DC, Lieth H (2005) Optimizing long-term water allocation in the Amudarya River Delta: a water management model for ecological impact assessment. Environ Model Softw 20:529–545
See L, Openshaw S (2009) A hybrid multi-model approach to river level forecasting. Hydrol Sci J 45(4):523–536
Stefan H, Andrew H (2008) A comparison of multiple criteria analysis techniques for water resource management. Eur J Oper Res 184:255–265
Sziło J, Bialik RJ (2017) Bedload transport in two creeks at the ice-free area of the Baranowski Glacier, King George Island, West Antarctica. Polish Polar Res 38(1):21–23. https://doi.org/10.1515/popore-2017-0003
Taormina R, Chau KW, Sivakumar B (2015) Neural network river forecasting through baseflow separation and binary-coded swarm optimization. J Hydrol 529(3):1788–1797. https://doi.org/10.1016/j.jhydrol.2015.08.008
Walling DE (1977) Assessing the accuracy of suspended SRCs for a small watershed. Water Resour Res 13:531–538. https://doi.org/10.1029/WR013i003p00531
Walling DE, Webb BW (1981) The reliability of suspended sediment load data. In: Erosion and sediment transport measurement, vol 133. IAHS Publication, IAHS Press, Wallingford, pp 177–194
Wang P, Linker L (1999) An alternative regression method for constituent loads from steams. Water Qual Ecosyst Model 4:935–942
Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3–4):294–306
Wang WC, Xu DM, Chau KW, Lei GJ (2014) Assessment of river water quality based on theory of variable fuzzy sets and fuzzy binary comparison method. Water Resour Manage 28(12):4183–4200. https://doi.org/10.1007/s11269-014-0738-4
Wu CL, Chau KW (2011) Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol 399(3–4):394–409. https://doi.org/10.1016/j.jhydrol.2011.01.017
Yang CT, Marsooli R, Aalami MT (2009) Evaluation of total load sediment transport formulas using ANN. Int J Sedim Res 24(3):274–286. https://doi.org/10.1016/S1001-6279(10)60003-0
Yarar A, Onur Yildiz M, Copty NK (2009) Modelling level change in lakes using Neuro-fuzzy and artificial neural networks. J Hydrol 365(3–4):329–334
Zimmermann HJ (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst 1:45–55
Acknowledgements
We thank TAMAB (Water Resources Research Organization of Iran) for providing the data for discharge and sediment and for helping us with the data preprocessing. This article is a result of a scientific work and has been extracted from a research project sponsored by Arak Branch, Islamic Azad University.
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Varvani, J., Khaleghi, M.R. A performance evaluation of neuro-fuzzy and regression methods in estimation of sediment load of selective rivers. Acta Geophys. 67, 205–214 (2019). https://doi.org/10.1007/s11600-018-0228-9
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DOI: https://doi.org/10.1007/s11600-018-0228-9