Volume 10, Issue 19 (5-2019)
jwmr 2019, 10(19): 181-193 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Tabatabaei M, Salehpour Jam A, Hosseini S A. (2019). Presenting a New Approach to Increase the Efficiency of the Sediment Rating Curve Model in Estimating Suspended Sediment Load in Watersheds (Case Study: Mahabad-Chai River, Lake Urmia Basin, West Azarbayejan Province, Iran). jwmr. 10(19), 181-193. doi:10.29252/jwmr.10.19.181
URL: http://jwmr.sanru.ac.ir/article-1-933-en.html
URL: http://jwmr.sanru.ac.ir/article-1-933-en.html
Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO)
Abstract: (3057 Views)
The estimation of the correct amount of suspended sediment has an important role in the optimal design of water structures, erosion studies and water quality studies. The sediment rating curve (SRC) is a conventional and well-known regression model. However, due to logarithmic transformations in calibrating this model, its estimated values are often less than actual values. In the present study, using the instantaneous flow discharge and suspended sediment load of Beytas hydrometric station in the Mahabad-Chai River, the SRC model was calibrated, and then using Non-dominated Sorting Genetic Algorithm II (NSGA-II), the coefficients of this model optimized again. This algorithm is an automatic procedure and can use different objective functions in the calibration process simultaneously. In this regard, in the calibration process of the model, four objective functions RMSE, MAE, NSE, and LOGE were used as pairwise combinations. According to the results of the model evaluation, the NSE and LOGE objective functions were selected as the best objective functions for optimization of the model. In order to increase the power of the model's generalization, the self-organizing map (SOM) neural network was used to cluster data and form two homogeneous data sets (calibration and evaluation sets) of 70% and 30% respectively. The results showed that the use of the NSGA II algorithm resulted in improved model efficiency so that the results are much more favorable than the other results of conventional SRC models (such as the rating curve of mean load within discharge classes, SRC models corrected by correction factors). In this regard, the error value (RMSE) of the test data set in the best model of the conventional SRC models was 383.65 tons/day, which was reduced by using the NSGA II algorithm to 102.94 tons/day. In sum, using the NSGA-II algorithm, we can optimize the coefficients of the SRC model, which is more efficient than the other conventional models.
Keywords: Artificial Neural Network, Clustering, Curve, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Sediment Rating Self-Organizing Map, Suspended Sediment
Type of Study: Research |
Subject:
فرسايش خاک و توليد رسوب
Received: 2018/04/4 | Revised: 2019/07/31 | Accepted: 2018/10/21 | Published: 2019/08/3
Received: 2018/04/4 | Revised: 2019/07/31 | Accepted: 2018/10/21 | Published: 2019/08/3
References
1. Altunkaynak, A. 2009. Sediment load prediction by genetic algorithms. Advances in Engineering Software, 40: 928-934. [DOI:10.1016/j.advengsoft.2008.12.009]
2. Aslani, M., R. Fazl Ola and M. Ahmadizadeh. 2015. Determination of Nash conceptual model parameter using auto calibration in Kasilian watershed. Journal of Watershed Management Research 6(12):21-28 (In Persian).
3. Bahmanesh, J., M. Mohammadpour and M.M. Bateni. 2017. Comparison of river suspended sediment load estimation, using regression and GA methods. Journal of Watershed Management Research 8(16):132-141 (In Persian).
4. Bekele, E.G. and J.W. Nicklow. 2007. Multi-objective automatic calibration of SWAT using NSGA-II. Journal of Hydrology, 341(3): 165-176. [DOI:10.1016/j.jhydrol.2007.05.014]
5. Buyukyildiz, M. and S.Y. Kumcu. 2017. An estimation of the suspended sediment load using adaptive network based fuzzy inference system, support vector machine and artificial neural network models. Water Resources Management, 31(4): 1343-1359. [DOI:10.1007/s11269-017-1581-1]
6. Choudhury, P. and B.S. Sil. 2010. Integrated water and sediment flow simulation and forecasting models for river reaches. Journal of Hydrology, 385(1): 313-322. [DOI:10.1016/j.jhydrol.2010.02.034]
7. Cohn, T.A., L.L. Delong, E.J. Gilroy, R.M. Hirsch and D.K. Wells. 1989. Estimating constituent loads. Water Resources Research, 25(5): 937-942. [DOI:10.1029/WR025i005p00937]
8. Criss, R.E. and W.E. Winston. 2008. Do Nash values have value? Discussion and alternate proposals. Hydrological Processes, 22(14): 2723. [DOI:10.1002/hyp.7072]
9. Danndhmhr, A., A. Olyiaie and M.A. Ghorbani. 2010. Suspended sediment load prediction based on river discharge and genetic programming method. Iranian journal of Watershed Management Researches Journal (Pajouhesh & Sazandegi), 88: 44-54 (In Persian).
10. Deb, K. 2001. Multi-objective optimization using evolutionary algorithms, Wiley, New York, USA, 512 pp.
11. Deb, K., A. Pratap, S. Agarwal and T. Meyarivan. 2002. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2): 182-197. [DOI:10.1109/4235.996017]
12. Duan, N. 1983. Smearing estimate: a nonparametric transformation method. Journal of the American Statistical Association, 78(383): 605-610. [DOI:10.1080/01621459.1983.10478017]
13. Ebtehaj, I. and H. Bonakdari. 2016. Assessment of evolutionary algorithms in predicting non-deposition sediment transport. Urban Water Journal, 13: 499-510. [DOI:10.1080/1573062X.2014.994003]
14. Efstratiadis, A. and D. Koutsoyiannis. 2010. One decade of multi-objective calibration approaches in hydrological modelling: a review. Hydrological Sciences Journal, 55(1): 58-78. [DOI:10.1080/02626660903526292]
15. Ercan, M.B. and J.L. Goodall. 2016. Design and implementation of a general software library for using NSGA-II with SWAT for multi-objective model calibration. Environmental Modelling and Software, 84:112-120. [DOI:10.1016/j.envsoft.2016.06.017]
16. Ferguson, R.I. 1986. River loads underestimated by rating curves. Water Resources Research, 22: 74-76. [DOI:10.1029/WR022i001p00074]
17. Gupta, H.V., S. Sorooshian and P.O. Yapo. 1998. Toward improved calibration of hydrologic models: multiple and noncommen surable measures of information. Water Resources and Research, 34(4): 751-763. [DOI:10.1029/97WR03495]
18. Gupta, H.V., S. Sorooshian and P.O. Yapo. 1999. Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2): 135-143. [DOI:10.1061/(ASCE)1084-0699(1999)4:2(135)]
19. Gupta, H.V., H. Kling, K.K. Yilmaz and G.F. Martineza. 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modeling. Journal of Hydrology (Amsterdam), 377(1-2): 80-91. [DOI:10.1016/j.jhydrol.2009.08.003]
20. Jansson, M.B. 1996. Estimating a sediment rating curve of the Reventazon river at Palomo using logged mean loads within discharge classes. Journal of Hydrology, 183(3-4): 227-241. [DOI:10.1016/0022-1694(95)02988-5]
21. Jones, K.R., O. Berney, D.P. Carr and E.C. Barrett. 1981. Arid zone hydrology for agricultural development. FAO Irrigation and Drainage Paper, Rome, Italy, 271 pp.
22. Kalteh, A.M. 2008. Rainfall-runoff modelling using artificial neural networks (ANNs): modeling and understanding. Caspian Journal of Environmental Sciences, 6: 53-58.
23. Kalteh, A.M., P. Hjorth and R. Berndtsson. 2008. Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application. Environmental Modeling and Software, 23: 835-845. [DOI:10.1016/j.envsoft.2007.10.001]
24. Kao, S.J., T.Y. Lee and J.D. Milliman. 2005. Calculating highly fluctuated suspended sediment fluxes from mountainous rivers in Taiwan. Terrestrial Atmospheric and Oceanic Sciences, 16: 653-675. [DOI:10.3319/TAO.2005.16.3.653(T)]
25. Kaufman, L. and P.J. Rousseeuw. 2009. Finding groups in data: an introduction to cluster analysis (Vol. 344), John Wiley & Sons, New Jersey, USA, 342 pp.
26. Koch, R.W. and G.M. Smillie. 1986. Comment on "River loads underestimated by rating curves" by RI Ferguson. Water Resources Research, 22(13): 2121-2122. [DOI:10.1029/WR022i013p02121]
27. Kohonen, T. 1982. Analysis of a simple self-organizing process. Biological Cybernetics, 44: 135-140. [DOI:10.1007/BF00317973]
28. Krause, P., D.P. Boyle and F. Bäse. 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5: 89-97. [DOI:10.5194/adgeo-5-89-2005]
29. Kuok, K.K., S. Harun and S.M. Shamsuddin. 2010. Particle swarm optimization feed forward neural network for modeling runoff. International Journal of Environmental Science and Technology, 7: 67-78. [DOI:10.1007/BF03326118]
30. Legates, D.R. and G.J. McCabe. 1999. Evaluating the use of "goodness‐of‐fit" measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1): 233-241. [DOI:10.1029/1998WR900018]
31. Li, X., M.H. Nour, D.W. Smith and E.E. Prepas. 2010. Neural networks modelling of nitrogen export: model development and application to unmonitored boreal forest watersheds. Environmental Technology, 31: 495-510. [DOI:10.1080/09593330903527880]
32. Madsen, H. 2000. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. Journal of Hydrology, 235(3): 276-288. [DOI:10.1016/S0022-1694(00)00279-1]
33. May, R.J., H.R. Maier and G.C. Dandy. 2010. Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Networks, 23: 283-294. [DOI:10.1016/j.neunet.2009.11.009]
34. Miller, D.M. 1984. Reducing transformation bias in curve fitting. The American Statistician, 38(2): 124-126. [DOI:10.1080/00031305.1984.10483180]
35. Mohammad Rezapour, O., P. Nourjou and M.J. Zeynali. 2016. Compression of genetic algorithm and particle swarm algorithm models for optimizing coefficients of sediment rating curve in the estimation of suspended sediment in Sistan river (Case Study: Kohak station). The Iranian Society of Irrigation and Water Engineering, 6: 76-89 (In Persian).
36. Muhammadi, A., G. Akbari and G. Azizzian. 2012. Suspended sediment concentration estimation using artificial neural networks and neural-fuzzy inference system case study: Karaj Dam. Indian Journal of Science and Technology, 5: 3188-3193.
37. Muleta, M.K. 2011. Model performance sensitivity to objective function during automated calibrations. Journal of Hydrologic Engineering, 17(6): 756-767. [DOI:10.1061/(ASCE)HE.1943-5584.0000497]
38. Nash, J.E. and J.V. Sutcliffe. 1970. River flow forecasting through conceptual models part I-A discussion of principles. Journal of Hydrology, 10(3): 282-290. [DOI:10.1016/0022-1694(70)90255-6]
39. Rodríguez-Blanco, M.L., M.M. Taboada-Castro, L. Palleiro-Suárez and M.T. Taboada-Castro. 2010. Temporal changes in suspended sediment transport in an Atlantic catchment, NW Spain. Geomorphology, 123: 181-188 [DOI:10.1016/j.geomorph.2010.07.015]
40. Schwefel, H.P.P. 1995. Evolution and optimum seeking: the sixth generation. John Wiley & Sons, Inc., New York, USA, 456 pp.
41. Srinivas, N. and K. Deb. 1994. Multiple objective optimizations using non-dominated sorting in genetic algorithms.Evolutionary Computation 2(2): 221-248. [DOI:10.1162/evco.1994.2.3.221]
42. Swain, R. and B. Sahoo. 2017. Mapping of heavy metal pollution in river water at daily time-scale using spatio-temporal fusion of MODIS-aqua and Landsat satellite imageries. Journal of Environmental Management, 192: 1-14. [DOI:10.1016/j.jenvman.2017.01.034]
43. Thomas, R.B. 1985. Estimating total suspended sediment yield with probability sampling. Water Resources Research, 21(9): 1381-1388. [DOI:10.1029/WR021i009p01381]
44. Ulke, A., G. Tayfur and S. Ozkul. 2009. Predicting suspended sediment loads and missing data for Gediz River, Turkey. Journal of Hydrologic Engineering, 14: 954-965. [DOI:10.1061/(ASCE)HE.1943-5584.0000060]
45. Veldhuizen, D.A.V. and G.B. Lamont. 2000. Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evolutionary Computation, 8(2): 125-147. [DOI:10.1162/106365600568158]
46. Vercruysse, K., R.C. Grabowski and R.J. Rickson. 2017. Suspended sediment transport dynamics in rivers: Multi-scale drivers of temporal variation. Earth-Science Reviews, 166: 38-52. [DOI:10.1016/j.earscirev.2016.12.016]
47. Yapo, P.O., H.V. Gupta and S. Sorooshian. 1998. Multi-objective global optimization for hydrologic models. Journal of Hydrology, 204(1-4): 83-97. [DOI:10.1016/S0022-1694(97)00107-8]
48. Yar Kiani, A. 2009. Intelligent Systems. Press Center of Poyesh Andisheh, Tehran, Iran, 260 pp (In Persian).
49. Yee, K.Y., A.K. Ray and G.P. Rangiah. 2003. Multi-objective optimization of industrial styrene reactor. Computers and Chemical Engineering, 27: 111-130. [DOI:10.1016/S0098-1354(02)00163-1]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
