A performance evaluation of neuro-fuzzy and regression methods in estimation of sediment load of selective rivers

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 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

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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

    Google Scholar 

  5. 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

    Article  Google Scholar 

  6. Bialik RJ, Czernuszenko W (2013) On the numerical analysis of bed-load transport of saltating grains. Int J Sediment Res 28:413–420

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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

    Article  Google Scholar 

  9. Boning WC (2001) Recommendations for use of retransformation methods in regression, models used to estimate sediment loads. http://water.Usgs.Gov

  10. 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

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. Cohn TA, Delong LL, Gilroy EJ, Hirsch RM, Wells DK (1989) Estimating constituent loads. Water Resour Res 25(5):937–942

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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

  15. 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

    Article  Google Scholar 

  16. Ferguson RI (1986) River loads underestimated by rating curves. Water Resour Res 22:74–76. https://doi.org/10.1029/WR022i001p00074

    Article  Google Scholar 

  17. 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

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. Gholami V (2013) The influence of deforestation on runoff generation and soil erosion (case study: Kasilian Watershed). J For Sci 59(7):272–278

    Article  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. Holtschlag DJ (2001) Optimal estimation of suspended-sediment concentrations in streams. Hydrol Process 15:1133–1156. https://doi.org/10.1002/hyp.207

    Article  Google Scholar 

  24. 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

    Article  Google Scholar 

  25. 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

    Article  Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. Jang JSR (1993) ANFIS – Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. 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

    Article  Google Scholar 

  30. Julien B (1994) Water quality management with imprecise information. Eur J Oper Res 76:15–27

    Article  Google Scholar 

  31. 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

    Article  Google Scholar 

  32. 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

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. 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

    Article  Google Scholar 

  35. Koch RW, Smillie GM (1986) Comment on “River loads underestimated by rating curves” by R. I. Ferguson. Water Resour Res 22(13):2121–2122

    Article  Google Scholar 

  36. Liu B (2000) Dependent-chance programming in fuzzy environments. Fuzzy Sets Syst 109:97–106

    Article  Google Scholar 

  37. 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

    Article  Google Scholar 

  38. 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

    Article  Google Scholar 

  39. 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

    Article  Google Scholar 

  40. 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)

    Article  Google Scholar 

  41. 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

    Article  Google Scholar 

  42. 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

    Article  Google Scholar 

  43. See L, Openshaw S (2009) A hybrid multi-model approach to river level forecasting. Hydrol Sci J 45(4):523–536

    Article  Google Scholar 

  44. Stefan H, Andrew H (2008) A comparison of multiple criteria analysis techniques for water resource management. Eur J Oper Res 184:255–265

    Article  Google Scholar 

  45. 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

    Article  Google Scholar 

  46. 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

    Article  Google Scholar 

  47. 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

    Article  Google Scholar 

  48. 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

    Google Scholar 

  49. Wang P, Linker L (1999) An alternative regression method for constituent loads from steams. Water Qual Ecosyst Model 4:935–942

    Google Scholar 

  50. 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

    Article  Google Scholar 

  51. 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

    Article  Google Scholar 

  52. 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

    Article  Google Scholar 

  53. 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

    Article  Google Scholar 

  54. 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

    Article  Google Scholar 

  55. Zimmermann HJ (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst 1:45–55

    Article  Google Scholar 

Download references

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to M. R. Khaleghi.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Sediment rating curve
  • Indexes of the accuracy and the precision
  • Tree regression model
  • Neuro-fuzzy
  • Suspended load