DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling

Journal of Hydrology

Volumn 409, Issue 3-4, 2011, Pages 596-611

  Abrahart, Robert J ^a   Mount, Nick J ^a   Ab Ghani, Ngahzaifa ^a,b   Clifford, Nicholas J ^c   Dawson, Christian W ^d  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^b UNIVERSITY MALAYSIA PAHANG   (Malaysia)

^c KING'S COLLEGE LONDON   (United Kingdom)

^d LOUGHBOROUGH UNIVERSITY   (United Kingdom)

Author keywords

Data driven model; Hydrologic context; Modelling protocol; Rating curve; Suspended sediment; Tropical catchment

Indexed keywords

DATA-DRIVEN MODEL; HYDROLOGIC CONTEXT; MODELLING PROTOCOL; RATING CURVE; TROPICAL CATCHMENT;

ANOXIC SEDIMENTS; CATCHMENTS; LINEAR REGRESSION; RATING; RUNOFF; SEDIMENTOLOGY; STATISTICAL TESTS;

SUSPENDED SEDIMENTS;

CATCHMENT; CONCENTRATION (COMPOSITION); DATA QUALITY; DATA SET; EMPIRICAL ANALYSIS; HYDROLOGICAL MODELING; MODEL TEST; PREDICTION; RATING CURVE; REGRESSION ANALYSIS; SEDIMENT TRANSPORT; SUSPENDED SEDIMENT; TROPICAL REGION;

PUERTO RICO;

EID: 80054703617     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2011.08.054     Document Type: Article

References (57)

1. Abrahart R.J., Ab Ghani N., Swan J. DISCUSSION of An explicit neural network formulation for evapotranspiration. J. Hydrol. Sci. 2009, 54:382-388.
2. Abrahart R.J., See L.M. Neural network modelling of non-linear hydrological relationships. Hydrol. Earth Syst. Sci. 2007, 11:1563-1579.
3. Abrahart R.J., See L.M., Dawson C.W., Shamseldin A.Y., Wilby R.L. Nearly two decades of neural network hydrologic modeling. Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting 2010, 267-346. World Scientific Publishing, Hackensack, NJ. B. Sivakumar, R. Berndtsson (Eds.).
4. Abrahart R.J., See L.M., Heppenstall A.J., White S.M. Neural network estimation of suspended sediment: potential pitfalls and future directions. Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications 2008, 139-161. Springer-Verlag, Berlin and Heidelberg. R.J. Abrahart, L.M. See, D.P. Solomatine (Eds.).
5. Alp M., Ciĝizoĝlu H.K. Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environ. Modell. Softw. 2007, 22:2-13.
6. Aytek A., Guven A., Yuce M.I., Aksoy H. An explicit neural network formulation for evapotranspiration. J. Hydrol. Sci. 2008, 53:893-904.
7. Aytek A., Kisi Ö. A genetic programming approach to suspended sediment modelling. J. Hydrol. 2008, 351:288-298.
8. Babovic V. Data mining in hydrology. Hydrol. Process. 2005, 19:1511-1515.
9. Beven K. A manifesto for the equifinality thesis. J. Hydrol. 2006, 320:18-36.
10. Buytaert W., Reusser D., Krause S., Renaud J-P. Why can't we do better than Topmodel?. Hydrol. Process. 2008, 22:4175-4179.
11. Campbell F.B., Bauder H.A. A rating-curve method for determining silt-discharge of streams. Trans. Am. Geophys. Union 1940, 21:603-607.
12. Ciĝizoĝlu H.K. Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv. Water Resour. 2004, 27:185-195.
13. Ciĝizoĝlu H.K., Alp M. Generalized regression neural network in modelling river sediment yield. Adv. Eng. Softw. 2006, 37:63-68.
14. Çimen M. Estimation of daily suspended sediments using support vector machines. J. Hydrol. Sci. 2008, 53:656-666.
15. Dawson C.W., Abrahart R.J., See L.M. HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ. Modell. Softw. 2007, 22:1034-1052.
16. Dawson C.W., Abrahart R.J., See L.M. HydroTest: further development of a web resource for the standardised assessment of hydrological models. Environ. Modell. Softw. 2010, 25:1481-1482.
17. Dawson C.W., Abrahart R.J., Shamseldin A.Y., Wilby R.L. Flood estimation at ungauged sites using artificial neural networks. J. Hydrol. 2006, 319:391-409.
18. Dawson C.W., Harpham C., Wilby R.L., Chen Y. An evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China. Hydrol. Earth Syst. Sci. 2002, 6:619-626.
19. Diaz-Ramirez J.N., Perez-Alegria L.R., McAnally W.H. Hydrology and sediment modeling using HSPF/BASINS in a Tropical Island Watershed. Trans. ASABE 2008, 51:1555-1565.
20. Dunne T. Sediment yield and land use in tropical catchments. J. Hydrol. 1979, 42:281-300.
21. Ferguson R.I. River loads underestimated by rating curves. Water Resour. Res. 1986, 22:74-76.
22. Glysson, G.D., 1987. Sediment-Transport Curves. US Geological Survey Open File Report 87-218, p. 47.
23. Giustolisi O., Laucelli D. Improving generalization of artificial neural networks in rainfall-runoff modelling. J. Hydrol. Sci. 2005, 50:439-457.
24. Helmer E.H., Ramos O., Lopez T., Del M., Quinones M., Diaz W. Mapping the forest type and land cover of Puerto Rico, a component of the Caribbean biodiversity hotspot. Carib. J. Sci. 2002, 38:165-183.
25. Ho, F.P., Scwerde, R.W., Goodyear, H.V., 1975. Some Climatological Characteristics of Hurricanes and Tropical Storms, Gulf and East Coast of the United States. NOAA Technical Report NWS15 (COM-7511-88).
26. Jain S.K. Development of integrated sediment rating curves using ANNs. ASCE J. Hydraul. Eng. 2001, 127:30-37.
27. Jain S.K. Development of integrated discharge and sediment rating relation using a compound neural network. ASCE J. Hydrol. Eng. 2008, 13:124-131.
28. Jang J-S.R. ANFIS: Adaptive-Network-based Fuzzy Inference System. IEEE Trans. Syst. Man Cybern 1993, 23:665-685.
29. Jang J-S.R., Sun C-T., Mizutani E. Neuro-Fuzzy and Soft Computing: a Computational Approach to Learning and Machine Intelligence 1997, Prentice Hall, Upper Saddle River, NJ, USA.
30. Kisi Ö. Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation. J. Hydrol. Sci. 2004, 49:1025-1040.
31. Kisi Ö. Daily suspended sediment modelling using a fuzzy differential evolution approach. J. Hydrol. Sci. 2004, 49:183-197.
32. Kisi Ö. Suspended sediment estimation using neuro-fuzzy and neural network approaches. J. Hydrol. Sci. 2005, 50:683-696.
33. Kisi Ö. Evolutionary fuzzy models for river suspended sediment concentration estimation. J. Hydrol. 2009, 372:68-79.
34. Kisi Ö. River suspended sediment concentration modeling using a neural differential evolution approach. J. Hydrol. 2010, 389:227-235.
35. Kisi Ö., Guven A. A machine code-based genetic programming for suspended sediment concentration estimation. Adv. Eng. Softw. 2010, 41:939-945.
36. Kisi Ö., Haktanir T., Ardiclioglu M., Ozturk O., Yalcin E., Uludag S. Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Adv. Eng. Softw. 2009, 40:438-444.
37. Kisi O., Karahan M.E., Sen Z. River suspended sediment modeling using fuzzy logic approach. Hydrol. Process. 2006, 20:4351-4362.
38. Kisi Ö., Yuksel I., Dogan E. Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques. J. Hydrol. Sci. 2008, 53:1270-1285.
39. Larsen, M.C., 1997. Tropical Geomorphology and Geomorphic Work: a Study of Geomorphic Processes and Sediment and Water Budgets in Montane Humid-Tropical Forested and Developed Watersheds, Puerto Rico. Unpublished Ph.D. Thesis, Geography Department, University of Colorado, Boulder, Colorado, USA, 341 pp.
40. Larsen M.C., Torres-Sanchez A.J. The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico. Geomorphology 1998, 24:309-331.
41. Lee H-Y., Lin Y-T., Chiu Y-J. Quantitative estimation of reservoir sedimentation from three typhoon events. ASCE J. Hydrol. Eng. 2006, 11:362-370.
42. Minns A.W., Hall M.J. Artificial neural networks as rainfall-runoff models. J. Hydrol. Sci. 1996, 41:399-417.
43. Mount, N.J., Abrahart, R.J., in press. Load or concentration, logged or unlogged? Addressing ten years of uncertainty in neural network suspended sediment prediction. Hydrological Processes. doi:10.1002/hyp.8033.
44. Neuman, C.J., Jarvinen, B.R., Pike, A.C., 1990. Tropical Cyclones of the North Atlantic Ocean, 1971-1986 (with Storm Tracks through 1989). National Climatic Data Center Historical Climatology Series 6, 186. pp.
45. Oreskes N. The role of quantitative models in science. Models in Ecosystem Science 2003, 13-31. Princeton University Press, Princeton, NJ, USA. C.D.W. Canham, J. Cole, W.K. Lauenroth (Eds.).
46. Parés-Ramos, I.K., Gould, W.A., Aide, T.M., 2008. Agricultural abandonment, suburban growth, and forest expansion in Puerto Rico between 1991 and 2000. Ecology and Society 13, 1 (online).
47. Partal T., Cigizoglu H.K. Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. J. Hydrol. 2008, 358:317-331.
48. Priddy K.L., Keller P.E. Artificial Neural Networks: an Introduction 2005, SPIE-International Society for Optical Engineering Press, Bellingham, WA, USA.
49. Rajaee T., Mirbagheri S.A., Nourani V., Alikhani A. Prediction of daily suspended sediment load using wavelet and neuro-fuzzy combined model. Int. J. Environ. Sci. Technol. 2010, 7:93-110.
50. Sayed T., Tavakolie A., Razavi A. Comparison of adaptive network based fuzzy inference systems and B-spline neuro-fuzzy mode choice models. J. Comput. Civil Eng. 2003, 17:123-130.
51. Scatena F.N., Larsen M.C. Physical aspects of Hurricane Hugo in Puerto Rico. Biotropica 1991, 23:317-323.
52. Şen Z., Altunkaynak A. Fuzzy awakening in rainfall-runoff modeling. Nord. Hydrol. 2003, 35:31-43.
53. Solomatine D.P., See L.M., Abrahart R.J. Data-driven modelling: concepts, approaches and experiences. Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Application 2008, 17-30. Springer-Verlag, Berlin and Heidelberg, Germany. R.J. Abrahart, L.M. See, D.P. Solomatine (Eds.).
54. Todini E. Hydrological catchment modelling: past, present and future. Hydrol. Earth Syst. Sci. 2007, 11:468-482.
55. USGS, 2000. Luquillo Mountains, Peurto Rico - a Water, Energy and Biogeochemical Budgets Program Site. USGS Fact Sheet 163-99.
56. Warne, A.G., Webb, R.M.T., Larsen, M.C., 2005. Water, Sediment, and Nutrient Discharge Characteristics of Rivers in Puerto Rico, and their Potential Influence on Coral Reefs. USGS Scientific Investigations Report 2005-5206, 186 pp.
57. Williams G.P. Sediment concentration versus water discharge during single hydrologic events in rivers. J. Hydrol. 1989, 111:89-106.