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تحلیل عدم قطعیت پارامترهای مدل SVM برای برآورد بار رسوبات معلق و بستر در ایستگاه سیرا کرج با روش شبیهسازی مونت کارلو | ||
تحقیقات آب و خاک ایران | ||
دوره 52، شماره 1، فروردین 1400، صفحه 195-212 اصل مقاله (1.56 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2020.308225.668704 | ||
نویسندگان | ||
علیرضا کیهانی1؛ علی محمد آخوندعلی* 2؛ حسین فتحیان3 | ||
1گروه هیدرولوژی و منابع آب، دانشکده مهندسی علوم آب، دانشگاه شهید چمران اهواز، اهواز، ایران. | ||
2استاد گروه هیدرولوژی و منابع آب، دانشکده مهندسی علوم آب، دانشگاه شهید چمران اهواز، اهواز، ایران | ||
3گروه مهندسی منابع آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران | ||
چکیده | ||
برآورد میزان رسوب حمل شده توسط جریان برای برنامهریزی و ذخیره منابع آب مخازن سدها و تغییرات بستر رودخانهها، مدیریت آبخیز، حفاظت سواحل و محیط زیست حائز اهمیت است. انتقال رسوب در رودخانه یک پدیده ذاتا غیرقطعی و پیچیده میباشد. دانش ناکامل در مورد فرآیندها و دادهها، عدمقطعیت در برآورد انتقال رسوب را ایجاد میکند. عدمقطعیت پارامترها، از جمله منابع اصلی عدمقطعیت در برآورد بار رسوبات معلق و بستر است. در این مقاله از روش شبیهسازی مونت کارلو برای برآورد عدمقطعیت بار رسوبات معلق و بستر بهعلت عدمقطعیت در پارامترهای مدل ماشین بردار پشتیبان (SVM) در حوضه سد کرج استفاده شده است. برای انتخاب متغیرهای ورودی موثر در مدل SVM برای برآورد بار رسوبات معلق و بستر، از الگوریتم PMI استفاده شد. نتایج بهکارگیری الگوریتم PMI نشان میدهد که تنها متغیر موثر در برآورد بار رسوبات معلق و بستر، دبی جریان در زمان حال است. نتایج نشان میدهد که عدمقطعیت در برآورد بار رسوب معلق با مدل SVM برای دادههای آموزش، آزمون و کل دادهها بهترتیب برابر با 8/12، 17 و 5/13 درصد است. همچنین عدمقطعیت در برآورد بار رسوب بستر با مدل SVM برای دادههای آموزش، آزمون و کل دادهها بهترتیب برابر با 5/23، 8/36 و 2/27 درصد است. بنابراین عدمقطعیت در برآورد بار رسوب بستر با مدل SVM بیشتر از عدمقطعیت در برآورد بار رسوب معلق است. بهکارگیری روشهای بهینهسازی میتواند برای برآورد دقیق مقادیر پارامترها و کاهش عدمقطعیت در برآورد بار رسوبات معلق و بستر مفید باشد. | ||
کلیدواژهها | ||
عدمقطعیت پارامترها؛ مدل SVM؛ بار رسوب معلق و بستر؛ الگوریتم PMI؛ مونت-کارلو | ||
عنوان مقاله [English] | ||
Uncertainty Analysis of SVM Model Parameters for Estimating Suspended and Bed Sediment Load at Sierra Station in Karaj by Monte-Carlo Simulation Method | ||
نویسندگان [English] | ||
Alireza Keihani1؛ Ali Mohammad Akhondali2؛ Hosein Fathian3 | ||
1Department of Hydrology and Water Resources, Collage of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. | ||
2Professor of Hydrology and Water Resources Engineering Department, Collage of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
3Department of Water Resources Engineering,, Ahvaz Branch, Islamic Azad University,, Ahvaz, Iran. | ||
چکیده [English] | ||
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. | ||
کلیدواژهها [English] | ||
Parameter Uncertainty, SVM model, Suspended and bed sediment load, PMI Algorithm, Monte-Carlo | ||
مراجع | ||
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.
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