Uncertainty Analysis of SVM Model Parameters for Estimating Suspended and Bed Sediment Load at Sierra Station in Karaj by Monte-Carlo Simulation Method

Document Type : Research Paper

Authors

1 Department of Hydrology and Water Resources, Collage of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Professor of Hydrology and Water Resources Engineering Department, Collage of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

3 Department of Water Resources Engineering,, Ahvaz Branch, Islamic Azad University,, Ahvaz, Iran.

Abstract

Estimation of sediment transported by the streamflow is important for planning and storing water resources of dam reservoirs and river bed changes, watershed management, coastal protection and the environment. Sediment transport in the river is an inherently uncertain and complex phenomenon. Incomplete knowledge of processes and data create uncertainty in estimating sediment transport. Parameters uncertainty is one of the main sources of uncertainty in estimating the suspended and bed sediment load. In this paper, the Monte Carlo (MC) simulation method is used to estimate the uncertainty of suspended and bed sediment load due to uncertainty in the parameters of the support vector machine (SVM) model in the Karaj Dam Basin. The partial mutual information (PMI) algorithm was used to select the efficient input variables in the SVM model to estimate the suspended and bed sediment load. The results of using PMI algorithm show that the only efficient variable in estimating the suspended and bed sediment loads is the current stream discharge. The results show that the uncertainty in estimating the suspended sediment load with SVM model for training, test and total data is equal to 12.8%, 17% and 13.5%, respectively. Also, the uncertainty in estimating the bed sediment load with SVM model for training, test and total data is equal to 23.5%, 36.8% and 27.2%, respectively. Therefore, the uncertainty in estimating the bed sediment load with SVM model is more than the one in estimating the suspended sediment load. Therefore, the use of optimization methods can be useful for accurate estimation of parameter values and reducing uncertainty in estimating the suspended and bed sediment load.

Keywords

References

Abrahart, R., Kneale, P.E. and See, L.M. (2004). Neural networks for hydrological modeling. CRC Press, 316p
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control 19:716–723
Adnan, R.M., Liang, Z., El-Shafie, A., Zounemat-Kermani, M. and Kisi, O. (2019). Prediction of Suspended Sediment Load Using Data-Driven Models. Water, 11(10), p.2060.
Bhavsar, P. N. and Patel, J. N. (2020). Event-based rainfall–run-off modeling and uncertainty analysis for lower Tapi Basin, India. ISH Journal of Hydraulic Engineering, 26(3), 353-362.
Bonacci, O. and Oskoruš, D. (2010). The changes in the lower Drava River water level, discharge and suspended sediment regime. Environmental Earth Sciences, 59(8), 1661-1670.
Bowden, G.J., Maier, H.R. and Dandy, G.C. (2005). Input determination for neural network models in water resources applications. Part 2. Case study: Forecasting salinity in a river. Journal of Hydrology, 301(1–4), 93–107
Chang, T. K., Talei, A., Alaghmand, S. and Ooi, M. P. L. (2017). Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques. Journal of Hydrology, 545, 100-108.
Cover, T.M. and Thomas, J.A. (1991). Elements of information theory. John Wiley & Sons, Inc., New York, 776p
     Dams, J., Nossent, J., Senbeta, T. B., Willems, P. and Batelaan, O. (2015). Multi-model approach to assess the impact of climate change on runoff. Journal of Hydrology, 529, 1601–1616.
David, F.N. (1966). Tables of the correlation coefficient. In: Pearson ES, Hartley HO (Eds.) Biometrika tables for statisticians, third ed., vol. 1. Cambridge University Press, Cambridge.
Eckhardt, K., Breuer, L., & Frede, H. G. (2003). Parameter uncertainty and the significance of simulated land use change effects. Journal of Hydrology, 273(1-4), 164-176.
Fang, W., Huang, S., Huang, Q., Huang, G., Meng, E., & Luan, J. (2018). Reference evapotranspiration forecasting based on local meteorological and global climate information screened by partial mutual information. Journal of Hydrology, 561, 764-779.
FathAbadi, A., Ruohani, H., Seyedian, S. M. (2018). The efficiency of nonparametric methods based on residual analizes and parametric method to estimate hydrological model uncertainty. Iran Water and Soil Research Journal, 49(2), 281-292. (In Farsi)
Fathian, H., AkhondAli, A.M., Sharifi, M.R. (2020). Parameters Uncertainty Analysis in Estimation of Probable Maximum Flood in Bakhtiary Dam Basin by Monte Carlo Method. Iran Water and Soil Research Journal, 51(4), 855-871. (In Farsi)
Gilroy, K. L. and McCuen, R. H. (2012). A nonstationary flood frequency analysis method to adjust for future climate change and urbanization. Journal of hydrology, 414, 40-48.
Goebel, B., Dawy, Z., Hagenauer, J. and Mueller, J. C. (2005). An approximation to the distribution of finite sample size mutual information estimates. In IEEE International Conference on Communications, 2, 1102-1106
     Isazadeh, M., Biazar, S. M. and Ashrafzadeh, A. (2017). Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters. Environmental Earth Sciences, 76(17), 1–14.
Jiang, C., Xiong, L., Xu, C. Y. and Guo, S. (2015). Bivariate frequency analysis of nonstationary low‐flow series based on the time‐varying copula. Hydrological Processes, 29(6), 1521-1534.
Karami cheme, E. and Mazaheri, M. (2018). Determine of the importance of longitude dispersion coefficient on solute transport in rivers using the Monte Carlo simulation. Iran Water and Soil Research Journal, 50(4), 763-776. (In Farsi)
Lee, D. H. and Kang, D. S. (2016). The application of the artificial neural network ensemble model for simulating streamflow. Procedia Engineering, 154, 1217–1224.
Liu, Y. and Gupta, H. V. (2007). Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework. Water Resources Research, 43, 1–18.
Miao, C., Ni, J., Borthwick, A. G. and Yang, L. (2011). A preliminary estimate of human and natural contributions to the changes in water discharge and sediment load in the Yellow River. Global and Planetary Change, 76(3-4), 196-205.
May, R. J., Dandy, G. C., Maier, H. R. and Fernando, T. G. (2006). Critical values of a kernel density-based mutual information estimator. In The 2006 IEEE International Joint Conference on Neural Network Proceedings, 4898-4903.
May, R. J., Maier, H. R., Dandy, G. C. and Fernando, T. G. (2008). Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modelling & Software, 23(10-11), 1312-1326.
Nash, J.E. and Sutcliffe, J.V. (1970). River flow forecasting through conceptual models; part I: A discussion of principles. Journal of Hydrology, 10, 282-290.
Nourani, V., Molajou, A., Tajbakhsh, A.D. and Najafi, H. (2019). A wavelet based data mining technique for suspended sediment load modeling. Water Resources Management, 33(5), 1769-1784.
Peng, J., Chen, S. and Dong, P. (2010). Temporal variation of sediment load in the Yellow River basin, China, and its impacts on the lower reaches and the river delta. Catena, 83(2-3), 135-147.
Pelletier, J. D. (2012). A spatially distributed model for the long‐term suspended sediment discharge and delivery ratio of drainage basins. Journal of Geophysical Research: Earth Surface, 117(F2).
Rodríguez-Blanco, M. L., Taboada-Castro, M. M., Palleiro, L. and Taboada-Castro, M. T. (2010). Temporal changes in suspended sediment transport in an Atlantic catchment, NW Spain. Geomorphology, 123(1-2), 181-188.
Rymszewicz, A., Bruen, M., O'Sullivan, J. J., Turner, J. N., Lawler, D. M., Harrington, J. R., Conroy, E. and Kelly-Quinn, M. (2018). Modelling spatial and temporal variations of annual suspended sediment yields from small agricultural catchments. Science of The Total Environment, 619, 672-684.
Salehpoor, j, Ashraf Zadeh, A. and Mosavi S.A. (2019).  Investigating the uncertainty of data-based models in forecasting monthly flow of the Hablehroud River. Iran Water and Soil Research Journal, (In Farsi)
Scholkopf, B. (2001). The kernel trick for distances. Advances in neural information processing systems, 301-307.
Shafeizadeh, M., Fathian, H., Nikbakht Shahbazi, A. (2019). Continuous rainfall-runoff simulation by artificial neural networks based on efficient input variables selection using partial mutual information (PMI) algorithm. Iran Water Resources Research, 15(2), 144-161. (In Farsi)
Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423
Sharma, A. (2000). Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: part 1: A strategy for system predictor identification. Journal of Hydrology, 239, 232–239
Shen, Z.Y., Chen, L., Chen, T. and Di Baldassarre, G. (2012). Analysis of parameter uncertainty in hydrological and sediment modeling using GLUE method: a case study of SWAT model applied to Three Gorges Reservoir Region, China. Hydrology & Earth System Sciences, 16(1), 121-132.
Shin, K. S., Lee, T. S., & Kim, H. J. (2005). An application of support vector machines in bankruptcy prediction model. Expert systems with applications, 28(1), 127-135.
Sharafati, A., Haji Seyed Asadollah, S.B., Motta, D. and Yaseen, Z.M. (2020). Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis. Hydrological Sciences Journal, 65(12), 2022-2042.
Syvitski, J. P., & Milliman, J. D. (2007). Geology, geography, and humans battle for dominance over the delivery of fluvial sediment to the coastal ocean. The Journal of Geology, 115(1), 1-19.
Tena, A., Batalla, R. J., Vericat, D. and López-Tarazón, J. A. (2011). Suspended sediment dynamics in a large regulated river over a 10-year period (the lower Ebro, NE Iberian Peninsula). Geomorphology, 125(1), 73-84.
Vanmaercke, M., Poesen, J., Broeckx, J. and Nyssen, J. (2014). Sediment yield in Africa. Earth-Science Reviews, 136, 350-368.
Vogel, R. M., Yaindl, C. and Walter, M. (2011). Nonstationarity: flood magnification and recurrence reduction factors in the United States 1. JAWRA Journal of the American Water Resources Association, 47(3), 464-474.
Walling, D. E. (2006). Human impact on land–ocean sediment transfer by the world's rivers. Geomorphology, 79(3-4), 192-216.
Walling, D. E. (2009). The impact of global change on erosion and sediment transport by rivers: current progress and future challenges. Unesco.
Wieprecht, S., Tolossa, H. G. and Yang, C. T. (2013). A neuro-fuzzy-based modelling approach for sediment transport computation. Hydrological sciences journal, 58(3), 587-599.
Zounemat-Kermani, M., Kişi, Ö., Adamowski, J. and Ramezani-Charmahineh, A. (2016). Evaluation of data driven models for river suspended sediment concentration modeling. Journal of Hydrology, 535, 457-472.
 
 
 
Iranian Journal of Soil and Water Research
Volume 52, Issue 1
April 2021
Pages 195-212

Files

Share

How to cite

Statistics