Published October 3, 2015 | Version 10002733
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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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  • P. G. Griffiths, R. Hereford, and R. H. Webb, "Sediment yield and runoff frequency of small drainage basins in the Mojave Desert, California and Nevada," ed, 2006.
  • M. J. Bender, L. F. Sawatsky, D. Long, and P. Anderson, A strategy for determining acceptable sediment yield for reclaimed mine lands. Italy: UNESCO, 2005.
  • R. Loch and D. Silburn, "Constraints to sustainability—soil erosion," Sustainable Crop Production in the Sub-tropics: an Australian Perspective. QDPI, 1996.
  • M. Nasseri, A. Moeini, and M. Tabesh, "Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming," Expert Systems with Applications, vol. 38, pp. 7387- 7395, 6// 2011.
  • D. P. Loucks and E. V. Beek, An introduction to methods,models,and application., 2005.
  • W. S. Merritt, R. A. Letcher, and A. J. Jakeman, "A review of erosion and sediment transport models," Environmental Modelling & Software, vol. 18, pp. 761-799, 2003.
  • H. Wheater, A. Jakeman, and K. Beven, "Progress and directions in rainfall-runoff modelling," 1993.
  • A. J. Jakeman, T. R. Green, S. G. Beavis, L. Zhang, C. R. Dietrich, and P. F. Crapper, " Modelling upland and in-stream erosion, sediment and phosphorus transport in a large catchment," Hydrological Processes vol. 13, pp. 745–752, 1999.
  • M. Kouli, P. Soupios, and F. Vallianatos, "Soil erosion prediction using the revised universal soil loss equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece," Environmental Geology, vol. 57, pp. 483-497, 2009. [10] V. Garg, "Modeling catchment sediment yield: a genetic programming 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," Hydrological processes, vol. 22, pp. 623-628, 2008. [40] T. Mulvaney, "On the use of self-registering rain and flood gauges in making observations of the relations of rainfall and flood discharges in a given catchment," Proceedings of the institution of Civil Engineers of Ireland, vol. 4, pp. 18-33, 1851. [41] Y. M. Chiang and F. J. Chang, "Integrating hydrometeorological information for rainfall‐runoff modelling by artificial neural networks," Hydrological Processes, vol. 23, pp. 1650-1659, 2009. [42] F. Anctil, C. Perrin, and V. Andreassian, "ANN output updating of lumped conceptual rainfall/runoff forecasting models1," ed: Wiley Online Library, 2003. [43] V. P. Singh and D. A. Woolhiser, "Mathematical modeling of watershed hydrology," Journal of hydrologic engineering, vol. 7, pp. 270-292, 2002. [44] D. Solomatine and A. Ostfeld, "Data-driven modelling: some past experiences and new approaches," Journal of hydroinformatics, vol. 10, pp. 3-22, 2008. [45] K. Beven, A. Calver, and E. Morris, "The Institute of Hydrology distributed model," 1987. [46] D. Yang, S. Herath, and K. Musiake, "Development of a geomorphology-based hydrological model for large catchments," Annual Journal of Hydraulic Engineering, JSCE, vol. 42, pp. 169-174, 1998. [47] S. Bergström and V. Singh, "The HBV model," Computer models of watershed hydrology., pp. 443-476, 1995. [48] V. P. Singh, Computer models of watershed hydrology: Water Resources Publications, 1995. [49] B. Yegnanarayana, Artificial neural networks: PHI Learning Pvt. Ltd., 2009. [50] J. Dorado, J. R. RabuñAL, A. Pazos, D. Rivero, A. Santos, and J. Puertas, "Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ANN and GP," Applied Artificial Intelligence, vol. 17, pp. 329-343, 2003. [51] J. Shiri and Ö. Kişi, "Comparison of genetic programming with neurofuzzy systems for predicting short-term water table depth fluctuations," Computers & Geosciences, vol. 37, pp. 1692-1701, 2011. [52] G. Tayfur, Soft computing in water resources engineering: artificial neural networks, fuzzy logic and genetic algorithms: WIT Press/Computational Mechanics, 2011. [53] F. Modaresi and S. Araghinejad, "A Comparative Assessment of Support Vector Machines, Probabilistic Neural Networks, and K-Nearest Neighbor Algorithms for Water Quality Classification," Water Resources Management, vol. 28, pp. 4095-4111, 2014. [54] B. Scholkopf, A. Smola, R. C. Williamson, and P. Bartlett, "New support vector algorithms," Neural Computation, vol. 12, pp. 1207– 1245, 2000. [55] R. S. Govindaraju and A. R. Rao, Artificial neural networks in hydrology: Springer Publishing Company, Incorporated, 2010. [56] J. D. Salas, Applied modeling of hydrologic time series: Water Resources Publication, 1980. [57] B. Sivakumar and R. Berndtsson, "Nonlinear Dynamic and Chaos in Hydrology," in Advances in data-based approaches for hydrologic modeling and forecasting, B. Sivakumar and R. Berndtsson, Eds., ed Singapore: World Scientific, 2010, pp. 411-461. [58] C. Wu, "Hydrological predictions using data-driven models coupled with data preprocessing techniques," The Hong Kong Polytechnic University, 2010. [59] G. E. Box, G. M. Jenkins, and G. C. Reinsel, Time series analysis: forecasting and control: John Wiley & Sons, 2013. [60] R. Poli and J. Koza, Genetic Programming: Springer, 2014. [61] K. Ahmadaali, A. Liaghat, O. B. Haddad, and N. Heydari, "Estimation of Virtual Water Using Support Vector Machine, K-nearest neighbour, and Radial Basis Function Neural Network Models," International Journal of Agronomy and Plant Production, vol. 4, pp. 2926-2936, 2013. [62] A. Danandeh Mehr, E. Kahya, and E. Olyaie, "Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique," Journal of Hydrology, vol. 505, pp. 240-249, 2013. [63] J. Sreekanth and B. Datta, "Comparative evaluation of genetic programming and neural network as potential surrogate models for coastal aquifer management," Water resources management, vol. 25, pp. 3201-3218, 2011. [64] J. R. Koza, Genetic programming: on the programming of computers by means of natural selection vol. 1: MIT press, 1992. [65] R. Poli, W. W. B. Langdon, N. F. McPhee, and J. R. Koza, A field guide to genetic programming: Lulu. com, 2008. [66] E. K. Burke and G. Kendall, Search methodologies: introductory tutorials in optimization and decision support techniques: Springer, 2005. [67] W. Banzhaf, P, R. E. Keller, and F. D. Francone, Genetic programming: an introduction. San Francisco (CA): Morgan Kaufmann, 1998. [68] O. Giustolisi, "Using genetic programming to determine Chezy resistance coefficient in corrugated channels," Journal of Hydroinformatics, vol. 6, pp. 157-173, 2004. [69] A. P. Mitra, A. A. Almal, B. George, D. W. Fry, P. F. Lenehan, V. Pagliarulo, et al., "The use of genetic programming in the analysis of quantitative gene expression profiles for identification of nodal status in bladder cancer," BMC cancer, vol. 6, p. 159, 2006. [70] A. Makkeasorn, N.-B. Chang, and J. Li, "Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed," Journal of Environmental Management, vol. 90, pp. 1069-1080, 2009. [71] R. Nunkesser, T. Bernholt, H. Schwender, K. Ickstadt, and I. Wegener, "Detecting high-order interactions of single nucleotide polymorphisms using genetic programming," Bioinformatics, vol. 23, pp. 3280-3288, 2007. [72] L. Zhang, L. B Jack, and A. K. Nandi, "Fault detection using genetic programming," Mechanical Systems and Signal Processing, vol. 19, pp. 271-289, 2005. [73] D. A. Savic, G. A. Walters, and J. W. Davidson, "A genetic programming approach to rainfall-runoff modelling," Water Resources Management, vol. 13, pp. 219-231, 1999. [74] J. A. Zyserman and J. Fredsøe, "Data analysis of bed concentration of suspended sediment," Journal of Hydraulic Engineering, vol. 120, pp. 1021-1042, 1994. [75] V. Babovic, M. Keijzer, D. Aguilera, and J. Harrington, "Automatic discovery of settling velocity equations," D2K Technical Rep, p. 1, 2001. [76] S. Y. Liong, T. R. Gautam, S. T. Khu, V. Babovic, M. Keijzer, and N. Muttil, "GENETIC PROGRAMMING: A NEW PARADIGM IN RAINFALL RUNOFF MODELING1," JAWRA Journal of the American Water Resources Association, vol. 38, pp. 705-718, 2002. [77] E. Harris, V. Babovic, and R. Falconer, "Velocity predictions in compound channels with vegetated floodplains using genetic programming," International Journal of River Basin Management, vol. 1, pp. 117-123, 2003. [78] A. Johari, G. Habibagahi, and A. Ghahramani, "Prediction of soil–water characteristic curve using genetic programming," Journal of Geotechnical and Geoenvironmental Engineering, vol. 132, pp. 661- 665, 2006. [79] J. Rabunal, J. Puertas, J. Suarez, and D. Rivero, "Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks," Hydrological processes, vol. 21, pp. 476- 485, 2007. [80] O. Kisi and J. Shiri, "A comparison of genetic programming and ANFIS in forecasting daily, monthly and daily streamflows," in Proceedings of the international symposium on innovations in intelligent systems and applications, 2010, pp. 118–122. [81] J. Shiri, Ö. Kişi, G. Landeras, J. J. López, A. H. Nazemi, and L. C. Stuyt, "Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain)," Journal of Hydrology, vol. 414, pp. 302-316, 2012. [82] A. S. Kizhisseri, D. Simmonds, Y. Rafiq, and M. Borthwick, "An evolutionary computation approach to sediment transport modeling," in Fifth International Conference on Coastal Dynamics, 2005, pp. 4-8. [83] K. Ozgur and S. Jalal, "River suspended sediment estimation by climatic variables implication:Comparative study among soft computing techniques," Computers & Geosciences, vol. 43, pp. 73-82, 2012. [84] A. Guven and Ö. Kişi, "Estimation of suspended sediment yield in natural rivers using machine-coded linear genetic programming," Water resources management, vol. 25, pp. 691-704, 2011. [85] V. Garg and V. Jothiprakash, "Evaluation of reservoir sedimentation using data driven techniques," Applied Soft Computing, vol. 13, pp. 3567–3581, 2013. [86] O. Kisi, A. H. Dailr, M. Cimen, and J. Shiri, "Suspended sediment modeling using genetic programming and soft computing techniques," Journal of Hydrology, vol. 450-451, pp. 48-58, 2012. [87] O. Kisi and A. Guven, "A machine code-based genetic programming for suspended sediment concentration estimation," Advances in Engineering Software, vol. 41, pp. 939-945, 7// 2010. [88] J. McDermott, D. R. White, S. Luke, L. Manzoni, M. Castelli, L. Vanneschi, et al., "Genetic programming needs better benchmarks," in Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, 2012, pp. 791-798. [89] M. O'Neill, L. Vanneschi, S. Gustafson, and W. Banzhaf, "Open issues in genetic programming," Genetic Programming and Evolvable Machines, vol. 11, pp. 339-363, 2010. [90] T. R. Naik and V. K. Dabhi, "Improving Generalization Ability of Genetic Programming: Comparative Study," arXiv preprint arXiv:1304.3779, 2013. [91] R. Poli, "Exact Schema Theory for Genetic Programming and Variablelength Genetic Algorithms with One-Point Crossover " Genet. Program. Evol. Mach. , vol. 2, p. 163, 2001. [92] R. Poli, L. Vanneschi, W. B. Langdon, and N. F. McPhee, "Theoretical results in genetic programming: the next ten year?," Genet Program Evolvable, pp. 285–320, 2010. [93] C. Ryan, J. Collins, and M. O. Neill, "Grammatical evolution: Evolving programs for an arbitrary language," in Genetic Programming, ed: Springer, 1998, pp. 83-96. [94] A. J. Owens, M. J. Walsh, and L. J. Fogel, Artificial intelligence through simulated evolution, 1966. [95] J. F. Miller, "An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach," in Proceedings of the Genetic and Evolutionary Computation Conference, 1999, pp. 1135-1142. [96] V. Nourani, R. G. Ejlali, and M. T. Alami, "Spatiotemporal groundwater level forecasting in coastal aquifers by hybrid artificial neural networkgeostatistics model: a case study," Environmental Engineering Science, vol. 28, pp. 217-228, 2011. [97] T. Rajaee, V. Nourani, M. Zounemat-Kermani, and O. Kisi, "River suspended sediment load prediction: Application of ANN and wavelet conjunction model," Journal of Hydrologic Engineering, vol. 16, pp. 613-627, 2010. [98] O. Kisi and J. Shiri, "Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models," Water resources management, vol. 25, pp. 3135-3152, 2011.