Predictive Modelling Techniques in Sediment Yield and Hydrological Modelling
This paper presents an extensive review of literature
relevant to the modelling techniques adopted in sediment yield and
hydrological modelling. Several studies relating to sediment yield are
discussed. Many research areas of sedimentation in rivers, runoff and
reservoirs are presented. Different types of hydrological models,
different methods employed in selecting appropriate models for
different case studies are analysed. Applications of evolutionary
algorithms and artificial intelligence techniques are discussed and
compared especially in water resources management and modelling.
This review concentrates on Genetic Programming (GP) and fully
discusses its theories and applications. The successful applications of
GP as a soft computing technique were reviewed in sediment
modelling. Some fundamental issues such as benchmark,
generalization ability, bloat, over-fitting and other open issues
relating to the working principles of GP are highlighted. This paper
concludes with the identification of some research gaps in
hydrological modelling and sediment yield.
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runoff frequency of small drainage basins in the Mojave Desert,
California and Nevada," ed, 2006.
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Perspective. QDPI, 1996.
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water demand using Extended Kalman Filter and Genetic
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rainfall-runoff modelling," 1993.
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P. F. Crapper, " Modelling upland and in-stream erosion, sediment and
phosphorus transport in a large catchment," Hydrological Processes vol.
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pp. 483-497, 2009.
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approach," Natural Hazards, 2011.
[11] S. Sorooshian, "Parameter estimation, model identification, and model
validation: conceptual-type models," in Recent advances in the modeling
of hydrologic systems, ed: Springer, 1991, pp. 443-467.
[12] M. B. Abbott, J. C. Bathurst, J. A. Cunge, P. E. O’Connell, and J.
Rasmussen, "An introduction to the European Hydrological System—
Systeme Hydrologique Europeen, SHE. 1. History and philosophy of a
physically-based, distributed modelling system," Journal ofHydrology,
vol. 87, pp. 45–59., 1986.
[13] A. Jakeman and G. Hornberger, "How much complexity is warranted in
a rainfall‐runoff model?," Water Resources Research, vol. 29, pp. 2637-
2649, 1993.
[14] R. C. Spear, "Large simulation models: calibration, uniqueness and
goodness of fit," Environmental Modelling & Software, vol. 12, pp. 219-
228, 1997.
[15] F. Kleissen, M. Beck, and H. Wheater, "The identifiability of conceptual
hydrochemical models," Water Resources Research, vol. 26, pp. 2979-
2992, 1990.
[16] M. B. Beck, "Water quality modeling: a review of the analysis of
uncertainty," Water Resources Research, vol. 23, pp. 1393-1442, 1987.
[17] J. P. Bennett, "Concepts of mathematical modeling of sediment yield,"
Water Resources Research, vol. 10, pp. 485-492, 1974.
[18] M. B. Beck, A. J. Jakeman, and M. J. McAleer, "Construction and
evaluation of models of environmental systems," In: Beck, M.B.,
McAleer, M.J. (Eds.), Modelling Change in Environmental Systems.John
Wiley and Sons, pp. pp. 3–35, 1995.
[19] J. D. Kalma and M. Sivapalan, Scale issues in hydrological modelling:
John Wiley and Sons, 1995.
[20] D. K. Borah, "Hydrologic procedures of storm event watershed models:
a comprehensive review and comparison," Hydrological Processes, vol.
25, pp. 3472-3489, 2011.
[21] N. R. Pradhan, C. W. Downer, and B. E. Johnson, "A physics based
hydrologic modeling approach to simulate non-point source pollution for
the purposes of calculating TMDLs and designing abatement measures,"
in Practical Aspects of Computational Chemistry III, ed: Springer, 2014,
pp. 249-282.
[22] S. Kim, D.-J. Seo, H. Riazi, and C. Shin, "Improving water quality
forecasting via data assimilation–Application of maximum likelihood
ensemble filter to HSPF," Journal of Hydrology, vol. 519, pp. 2797-
2809, 2014.
[23] A. K. Sajjan, Y. Gyasi-Agyei, and R. H. Sharma, "Modeling Grass-
Cover Effects on Soil Erosion on Railway Embankment Steep Slopes,"
Journal of Hydrologic Engineering, 2014.
[24] I. B. Karlsson, T. O. Sonnenborg, J. C. Refsgaard, and K. H. Jensen,
"Significance of hydrological model choice and land use changes when
doing climate change impact assessment," 2014.
[25] J. jeanne Huang, X. Lin, J. Wang, and H. Wang, "The precipitation
driven correlation based mapping method (PCM) for identifying the
critical source areas of non-point source pollution," Journal of
Hydrology, 2015.
[26] R. A. Letcher, A. J. Jakeman, W. S. Merritt, L. J. McKee, B. D. Eyre,
and B. Baginska, "Review of techniques to estimate catchment exports,"
1999.
[27] C. Perrin, C. Michel, and V. Andréassian, "Does a large number of
parameters enhance model performance? Comparative assessment of
common catchment model structures on 429 catchments," Journal of
Hydrology, vol. 242, pp. 275-301, 2001.
[28] M. Thorsen, J. Refsgaard, S. Hansen, E. Pebesma, J. Jensen, and S.
Kleeschulte, "Assessment of uncertainty in simulation of nitrate leaching
to aquifers at catchment scale," Journal of Hydrology, vol. 242, pp. 210-
227, 2001.
[29] C. Adami, Introduction to artificial life: Springer, 1998.
[30] W. Banzhaf, "Evolutionary Computation and Genetic Programming,"
2012.
[31] A. Mellit and S. A. Kalogirou, "Artificial intelligence techniques for
photovoltaic applications: A review," Progress in Energy and
Combustion Science, vol. 34, pp. 574-632, 2008.
[32] A. S. Tokar and P. A. Johnson, "Rainfall-runoff modeling using artificial
neural networks," Journal of Hydrologic Engineering, vol. 4, pp. 232-
239, 1999.
[33] J. Lin and F. L. Lewis, "Two-time scale fuzzy logic controller of flexible
link robot arm," Fuzzy sets and systems, vol. 139, pp. 125-149, 2003.
[34] P. Vas, Artificial-intelligence-based electrical machines and drives:
application of fuzzy, neural, fuzzy-neural, and genetic-algorithm-based
techniques vol. 45: Oxford University Press, 1999.
[35] A. Aytek and O. Kisi, "A genetic programming approach to suspended
sediment modelling," Journal of Hydrology, vol. 351, pp. 288– 298,
2008. [36] J. Smith and R. N. Eli, "Neural-network models of rainfall-runoff
process," Journal of water resources planning and management, vol.
121, pp. 499-508, 1995.
[37] D. Ömer Faruk, "A hybrid neural network and ARIMA model for water
quality time series prediction," Engineering Applications of Artificial
Intelligence, vol. 23, pp. 586-594, 2010.
[38] J. Lloret, "Underwater sensor nodes and networks," Sensors, vol. 13, pp.
11782-11796, 2013.
[39] C. Sivapragasam, R. Maheswaran, and V. Venkatesh, "Genetic
programming approach for flood routing in natural channels,"
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@article{"International Journal of Earth, Energy and Environmental Sciences:71223", author = "Adesoji T. Jaiyeola and Josiah Adeyemo", title = "Predictive Modelling Techniques in Sediment Yield and Hydrological Modelling", abstract = "This paper presents an extensive review of literature
relevant to the modelling techniques adopted in sediment yield and
hydrological modelling. Several studies relating to sediment yield are
discussed. Many research areas of sedimentation in rivers, runoff and
reservoirs are presented. Different types of hydrological models,
different methods employed in selecting appropriate models for
different case studies are analysed. Applications of evolutionary
algorithms and artificial intelligence techniques are discussed and
compared especially in water resources management and modelling.
This review concentrates on Genetic Programming (GP) and fully
discusses its theories and applications. The successful applications of
GP as a soft computing technique were reviewed in sediment
modelling. Some fundamental issues such as benchmark,
generalization ability, bloat, over-fitting and other open issues
relating to the working principles of GP are highlighted. This paper
concludes with the identification of some research gaps in
hydrological modelling and sediment yield.", keywords = "Artificial intelligence, evolutionary algorithm,
genetic programming, sediment yield. ", volume = "9", number = "9", pages = "1109-9", }
