Neural Network Estimation of Suspended Sediment: Potential Pitfalls and Future Directions

  • R.J. Abrahart
  • L.M. See
  • A.J. Heppenstall
  • S.M. White
Chapter
Part of the Water Science and Technology Library book series (WSTL, volume 68)

Abstract

This chapter examines two neural network approaches for modelling suspended sediment concentration at different temporal scales: daily-record and flood-event. Four daily-record models are developed for the USGS gauging station at Quebrada Blanca near Jagual in Puerto Rico previously used by kisi (2005) for estimating suspended sediment concentration: comparisons with that earlier investigation are presented. The flood-event approach is trialled on records for the EA gauging station at Low Moor on the River Tees in northern England. The power of neural networks to perform different types of modelling operation and to develop reasonable results in the two test cases is highlighted. Event-based modelling of mean suspended sediment concentration is a novel concept that warrants further trialling and testing on different international catchments or data sets.

Keywords

Sediment modelling neural network hysteresis 
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References

  1. Abrahart RJ, Heppenstall, A.J, See LM (2007) Timing error correction procedure applied to neural network rainfall-runoff modelling. Hydrological Sciences Journal 52(3): 1–12.CrossRefGoogle Scholar
  2. Abrahart RJ, See LM (2007a) Neural network emulation of a rainfall-runoff model. Hydrology and Earth System Sciences Discussions 4: 287–326.Google Scholar
  3. Abrahart RJ, See LM (2007b) Neural network modelling of non-linear hydrological relationships. Hydrology and Earth System Sciences 11(5): 1563–1579.Google Scholar
  4. Abrahart RJ, White SM (2001) Modelling Sediment Transfer in Malawi. Comparing Backpropagation Neural Network Solutions Against a Multiple Linear Regression Benchmark Using Small Data Sets. Physics and. Chemistry of the Earth (B) 26(1): 19–24.Google Scholar
  5. Agarwal A, Singh RD, Mishra SK, Bhunya PK (2005) ANN-based sediment yield models for Vamsadhara river basin (India). Water SA 31(1): 95–100.Google Scholar
  6. Asselman NEM (2000) Fitting and interpretation of sediment rating curves. Journal of Hydrology 234(3–4): 228–248.CrossRefGoogle Scholar
  7. Bellwood DR, Hughes TP, Folke C, Nyström M (2004) Confronting the coral reef crisis. Nature 429(24): 827–833.CrossRefGoogle Scholar
  8. Boose ER, Serrano MI, Foster DR. (2004) Landscape and Regional Impacts of Hurricanes in Puerto Rico, Ecological Monographs 74(2): 335–352.CrossRefGoogle Scholar
  9. Chikita KH, Kemnitz R, Kumai R (2002) Characteristics of sediment discharge in the subarctic Yukon River Alaska. Catena 48(4): 235–253.CrossRefGoogle Scholar
  10. Cigizoglu HK (2002a) Suspended sediment estimation and forecasting using artificial neural networks. Turkish Journal of Engineering and Environmental Sciences 26(1): 16–26.Google Scholar
  11. Cigizoglu HK (2002b) Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves. Turkish Journal of Engineering and Environmental Sciences 26(1): 27–36.Google Scholar
  12. Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Advances in Water Resources 27(2): 185–195.CrossRefGoogle Scholar
  13. Cigizoglu HK, Kisi O (2006) Methods to improve the neural network performance in suspended sediment estimation. Journal of Hydrology 317(3–4): 221–238.Google Scholar
  14. Coppus R, Imeson AC (2002) Extreme events controlling erosion and sediment transport in a semi-arid sub-Andean Valley. Earth Surface Processes and Landforms 27(13): 1365–1375.CrossRefGoogle Scholar
  15. Crowder DW, Demissie M, Markus M (2007) The accuracy of sediment loads when log-transformation produces nonlinear sediment load-discharge relationships. Journal of Hydrology 336(3–4): 250–268.CrossRefGoogle Scholar
  16. Curry B, Morgan PH (2003) Neural networks, linear functions and neglected non-linearity. Computational Management Science 1(1): 15–29.CrossRefGoogle Scholar
  17. Dawson CW, See LM, Abrahart RJ, Heppenstall AJ (2006) Symbiotic adaptive neuro-evolution applied to rainfall-runoff modelling in northern England. Neural Networks 19(2): 236–247.CrossRefGoogle Scholar
  18. De Vries A, Klavers HC (1994) Riverine fluxes of pollutants: monitoring strategy first, calculation methods second. European Journal of Water Pollution Control 4(2): 12–17.Google Scholar
  19. Duan N (1983) Smearing estimate: a nonparametric retransformation method. Journal of the American Statistical Society 78(383): 605–610.Google Scholar
  20. Fenn CR, Gurnell AM, Beecroft IR (1985) An evaluation of the use of suspended sediment rating curves for the prediction of suspended sediment concentration in a proglacial stream. Geografiska Annaler (A) 67(1–2): 71–82.CrossRefGoogle Scholar
  21. Ferguson RI (1986) River loads underestimated by rating curves. Water Resources Research 22(1): 74–76.CrossRefGoogle Scholar
  22. Fransen PJB, Phillips CJ, Fahey BD (2001) Forest road erosion in New Zealand: overview. Earth Surface Processes and Landforms 26(2): 165–174.CrossRefGoogle Scholar
  23. Giustolisi O, Laucelli D (2005) Increasing generalisation of input-output artificial neural networks in rainfall-runoff modelling. Hydrological Sciences Journal 50(3): 439–457.CrossRefGoogle Scholar
  24. Hansen NC, Daniel TC, Sharpley AN, Lemunyon JL (2002) The fate and transport of phosphorus in agricultural systems. Journal of Soil Water Conservation 57(6): 408–417.Google Scholar
  25. Hao C, Qiangguo C (2006) Impact of hillslope vegetation restoration on gully erosion induced sediment yield. Science in China Series D: Earth Sciences 49(2): 176–192.CrossRefGoogle Scholar
  26. Heppenstall AJ, See LM, Abrahart RJ, Dawson CW (2008) Neural Network Hydrological Modelling: An evolutionary approach. In: Abrahart RJ, See LM, Solomatine DP (eds) Practical Hydroinformatics: computational intelligence and technological developments in water applications. Springer-Verlag.Google Scholar
  27. Hicks, DM, Gomez B (2003) Sediment Transport. In: Kondolf GM, Piégay H (eds) Tools in Fluvial Geomorphology. John Wiley & Sons Ltd., Chichester, UK, 425–461.CrossRefGoogle Scholar
  28. Holtschlag DJ (2001) Optimal estimation of suspended-sediment concentrations in streams. Hydrological Processes 15(7): 1133–1155.CrossRefGoogle Scholar
  29. Horn J, Goldberg DE, Deb K (1994) Implicit niching in a learning classifier system: Nature’s way. Evolutionary Computation 2(1): 27–66.CrossRefGoogle Scholar
  30. Horowitz AJ (1995) The Use of Suspended Sediment and Associated Trace Elements in Water Quality Studies. IAHS Special Publication No. 4, IAHS Press: Wallingford, UK; 58 pp.Google Scholar
  31. Horowitz AJ (2003) An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations. Hydrological Processes 17(17): 3387–3409.CrossRefGoogle Scholar
  32. Jain SK (2001) Development of integrated sediment rating curves using ANNs. Journal of Hydraulic Engineering 127(1): 30–37.CrossRefGoogle Scholar
  33. Jang J-SR (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics 23(3): 665–685.CrossRefGoogle Scholar
  34. Keyes AM, Radcliffe D (2002) A Protocol for Establishing Sediment TMDLs. The Georgia Conservancy: Atlanta, GA, USA; 31 pp. http://www.georgiaconservancy.org/ WaterQuality/GA_CON%20QXD.pdfGoogle Scholar
  35. Kisi O (2004) Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrological Sciences Journal 49(6): 1025–1040.CrossRefGoogle Scholar
  36. Kisi O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrological Sciences Journal 50(4): 683–696.CrossRefGoogle Scholar
  37. Krishnaswamy J, Richter DD, Halpin PN, Hofmockel MS (2001) Spatial patterns of suspended sediment yields in a humid tropical watershed in Costa Rica. Hydrological Processes 15(12): 2237–2257.CrossRefGoogle Scholar
  38. Labadz JC, Butcher DP, Potter AWR, White P (1995) The delivery of sediment in upland reservoir systems. Physics and Chemistry of the Earth 20(2): 191–197.CrossRefGoogle Scholar
  39. Lenzi MA, Marchi L (2000) Suspended sediment load during floods in a small stream of the Dolomites (northeastern Italy). Catena 39(4): 267–282.CrossRefGoogle Scholar
  40. Lopes VL, Folliott PF, and Baker Jr MB (2001) Impacts of vegetative practices on suspended sediment from watersheds in Arizona. Journal of Water Resources Planning and Management 127(1): 41–47.CrossRefGoogle Scholar
  41. MacDonald LH, Sampson RW, Anderson DM (2001) Runoff and road erosion at the plot and road segment scales, St John, US Virgin Islands. Earth Surface Processes and Landforms 26(3): 251–272.CrossRefGoogle Scholar
  42. Marks SD, Rutt GP (1997) Fluvial sediment inputs to upland gravel bed rivers draining forested catchments: potential ecological impacts, Hydrology and Earth System Sciences 1(3): 499–508.CrossRefGoogle Scholar
  43. Merritt WS, Letcher RA, Jakeman AJ (2003) A review of erosion and sediment transport models. Environmental Modelling & Software 18(8–9): 761–799.CrossRefGoogle Scholar
  44. Morgan RPC (1986) Soil Erosion and Conservation. Longman, London.Google Scholar
  45. Moriarty DE, Miikkulainen R (1998) Forming neural networks through efficient and adaptive coevolution. Evolutionary Computation 5(4): 373–399.CrossRefGoogle Scholar
  46. Milner NJ, Scullion J, Carling PA, Crisp DT (1981) A review of the effects of discharge on sediment dynamics and consequent effects on invertebrates and salmonids in upland rivers. Advances in Applied Biology 6: 153–220.Google Scholar
  47. Nagy HM, Watanabe K, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering 128(6): 588–595.CrossRefGoogle Scholar
  48. Newcombe CP, Jensen JOT (1996) Channel suspended sediment and fisheries: a synthesis for quantitative assessment of risk and impact. North American Fisheries Management 16(4): 693–719.CrossRefGoogle Scholar
  49. Ottaway EM, Clarke A, Forrest DR (1981) Some Observations on Washout of Brown Trout (Salmo Trutta L.) Eggs in Teesdale Streams. Unpublished report, Freshwater Biological Association, Teesdale Unit, UK.Google Scholar
  50. Østrem G (1975) Sediment transport in glacial meltwater streams. In: Jopling AV, McDonald BC (eds) Glaciofluvial and Glaciolaustrine Sedimentation 23: 101–122 Society of Economic Paleontologists and Mineralogists Special Publication; 101pp.Google Scholar
  51. Phillips JM, Webb BW, Walling DE, Leeks GJL (1999) Estimating the suspended sediment loads of rivers in the LOIS study area using infrequent samples. Hydrological Processes 13(7): 1035–1050.CrossRefGoogle Scholar
  52. Potter MA (1997) The Design and Analysis of a Computational Model of Cooperative Coevolution. Unpublished PhD Thesis: George Mason University, Fairfax, Virginia, USA.Google Scholar
  53. Rosgen DL (1996) Applied River Morphology. Wildland Hydrology Books, Pagosa Springs, Colorado, USA.Google Scholar
  54. Rosgen DL (2001) A hierarchical river stability watershed-based sediment assessment methodology. In: Proceedings of the Seventh Federal Interagency Sedimentation Conference, Reno Nevada, 97–106.Google Scholar
  55. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagations. In: Rumelhart DE, McClelland JL (eds) Parallel Distributed Processing: Explorations in the Microstructures of Cognition. Vol 1. MIT Press, Cambridge, MA, USA, 318–362.Google Scholar
  56. Simon A, Hupp CR (1986) Channel Evolution in Modified Tennessee Streams. In: Proceedings of the Fourth Federal Interagency Sedimentation Conference, Las Vegas, Nevada, 5.71–5.82Google Scholar
  57. Stott T, Mount N (2004) Plantation forestry impacts on sediment yields and downstream channel dynamics in the UK: a review. Progress in Physical Geography 28(2): 197–240.CrossRefGoogle Scholar
  58. Sturges DL (1992) Streamflow and sediment transport responses to snow fencing a rangeland watershed. Water Resources Research 28(5): 1347–1356.CrossRefGoogle Scholar
  59. Thomas RB (1985) Estimating total suspended sediment yield with probability sampling. Water Resources Research 21(9): 1381–1388.CrossRefGoogle Scholar
  60. Thomas RB (1991) Systematic sampling for suspended sediment. In: Proceedings of the Fifth Federal Interagency Sedimentation Conference, Advisory Committee on Water Data, 2–17 to 2-24.Google Scholar
  61. Thomas RB, Lewis J (1993) A comparison of selection at list time and time-stratified sampling for estimating suspended sediment loads. Water Resources Research 29(4): 1247–1256.CrossRefGoogle Scholar
  62. Thomas RB, Lewis J (1995) An evaluation of flow-stratified sampling for estimating suspended sediment loads. Journal of Hydrology 170(1): 27–45.CrossRefGoogle Scholar
  63. US Environmental Protection Agency (1980) An Approach to Water Resource Evaluation of Non-point Silvicultural Sources. EPA-600/8-80-012, Athens, GA, USA, [http://www.epa.gov/warsss/rrisc/handbook.htm]Google Scholar
  64. Walling DE, Webb BW (1988) The reliability of rating curve estimates of suspended sediment yield; some further comments. In: Bordas MP, Walling DE (eds), Sediment Budgets. IAHS Publication No. 174, IAHS Press, Wallingford, UK, 337–350.Google Scholar
  65. Walling DE (1999) Linking land use, erosion and sediment yields in river basins. Hydrobiologia 410(1): 223–240.CrossRefGoogle Scholar
  66. Walling DE, Fang D (2003) Recent trends in the suspended sediment loads of the world rivers. Global Planetary Change 39(1–2): 111–126.CrossRefGoogle Scholar
  67. Warne AG, Webb RMT, Larsen MC (2005) Water, Sediment, and Nutrient Discharge Characteristics of Rivers in Puerto Rico, and their Potential Influence on Coral Reefs: U.S. Geological Survey Scientific Investigations Report 2005-5206, 58pp.Google Scholar
  68. White SM (2005) Sediment yield prediction and modelling. In: Anderson M. (ed) Encyclopaedia of Hydrological Sciences. John Wiley & Sons Ltd., Chichester, UK. doi:10.1002/0470848944.hsa089.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • R.J. Abrahart
    • 1
  • L.M. See
    • 2
  • A.J. Heppenstall
    • 2
  • S.M. White
    • 3
  1. 1.School of GeographyUniversity of NottinghamUniversity ParkUK
  2. 2.School of GeographyUniversity of LeedsWoodhouse LaneUK
  3. 3.Integrated Environmental Systems InstituteCranfield UniversityCranfieldUK

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