بررسی دقت شبکه پرسپترون چندلایه و تابع پایه شعاعی در برآورد میزان رسوب رودخانه (مطالعه موردی: زاینده رود)
محورهای موضوعی :
رسوب
رامتین صبح خیز فومنی
1
,
علیرضا مردوخ پور
2
1 - دانشجوی دکتری گروه مهندسی عمران، دانشگاه قم، قم، ایران.
2 - استادیار گروه مهندسی عمران، دانشگاه آزاد اسلامی واحد لاهیجان. *(مسوول مکاتبات)
تاریخ دریافت : 1399/08/08
تاریخ پذیرش : 1399/11/29
تاریخ انتشار : 1401/06/01
کلید واژه:
منحنی سنجه,
شبکه عصبی,
رودخانه,
رسوب گذاری,
چکیده مقاله :
زمینه و هدف: تخمین میزان رسوب به وسیله رودخانه یکی از مسائلی هست که مورد توجه محققان بسیاری از گذشته تاکنون قرار گرفته است. کاهش ظرفیت مخزن سد به وسیله رسوبات، اثرات مختلفی بر روی بخشهای مختلف گذاشته و سبب اثرات نامطلوب در حقابه هایی می شود که در بدو امر مورد توافق قرار گرفته اند که عواقب اقتصادی و خاص خود را خواهد دارد. هدف از این تحقیق بررسی میزان رسوب رودخانه با توجه به الگوریتم های شبکه عصبی و با استفاده از فرمول های تجربی موجود و همچنین روش های جدید موسوم به جعبه ی سیاه می باشد.
روش و بررسی: به منظور تخمین میزان رسوب از اطلاعات دبی سال های 1349 تا 1390 مربوط به رودخانه زاینده رود در ایستگاه اسکندری که یکی از ایستگاههای اندازه گیری های هیدرولوژیکی میباشد استفاده شده است. بدین منظور از دبی آب به عنوان ورودی و دبی رسوب به عنوان خروجی استفاده شده است.
یافته ها: از نتایج به دست آمده، این گونه استنباط می شود که شبکه RBF به دلیل داشتن خطای کمتر در مرحله آزمون دارای عملکرد بهتری است، اما با در نظر گرفتن سایر پارامترها و همچنین میزان خطا در مرحله ی TRAIN به نظر می رسد شبکه MLP دارای عملکرد بهتری است.
بحث و نتیجه گیری: در نهایت بعد از مدل سازی با استفاده از شبکه های عصبی و رابطه انیشتین و منحنی سنجه رسوب، این نتیجه بدست آمده است که برای تخمین میزان رسوب می توان به شبکه های عصبی اعتماد بیشتری داشت.
چکیده انگلیسی:
Background and Objective: Estimating the amount of sediment by the river is one of the topics that has been considered by many researchers since the past. Reduction of the dam reservoir capacity because of sediments has different effects on different sections and causes adverse effects on the water rights that were initially agreed upon, which will impose several economic and specific consequences. This study aims to model and estimate the amount of suspended sediment using existing experimental equations and new methods called black box.
Material and Methodology: The discharge (volumetric flow rate) related to Zayandehrud River in Eskandari station, one of the hydrological measuring stations, has been used to estimate the amount of sediment. For this purpose, water discharge and sediment rate are used as input and output, respectively.
Findings: According to the obtained results, it is concluded that the RBF network has better performance due to less error in the test stage, but the MLP network seems to have a better performance considering other parameters and the error in the TRAIN stage.
Discussion and Conclusion: Finally, after modeling by using neural networks, the Einstein relationship, and the sediment measurement curve, it is inferred that neural networks are more accurate to estimate the amount of sediment.
منابع و مأخذ:
Avarideh F, Bani Habib, M and Taher Shamsi, A (2001), Application of Artificial Neural Networks to Estimate River Sediment Flow, Third Iranian Hydraulic Conference, Tehran, University of Tehran, Faculty of Engineering. (In Persian)
Mesbahi, J, Chini, M, (1998), River Engineering Culture, Substantiation Plan of Technical Criteria and Standards of Water Industry, Ministry of Energy. (In Persian)
Asselman, N. E. M. (2000). Fitting and interpretation of sediment rating curves." Journal of Hydrology 234(3–4): 228-248.
Julien.P. Y. Erosion and sedimentation, (2010). Cambridge, UK: Cambridge University Press,
Kisi, O., (2004), Multi-layer perceptrons with Levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol. Sci. J. 49 (6), 1025–1040.
Kisi, O., Yuksel, I., Dogan, E., (2008) Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques. Hydrol. Sci. J. 53 (6), 1270–1285.
Eivani, Z., M.M. Ahmadi and K. Qaderi. 2016. Estimation of suspended sediment load concentration in river system using Group Method of Data Handling (GMDH). Journal of Watershed Management Research, 7(13): 218-229. (In Persian)
Kia, E., A. Emadi and R. Fazlola. 2015. Investigation for application of Adaptive Neuro-Fuzzy Inference System (ANFIS) in Babolroud suspended sediment load estimation. Journal of Watershed Management Research, 6(11): 15-23. (In Persian)
Kitsikoudis, V. and V. Hrissanthou. 2013. Derivation of sediment transport models for sand bed rivers from data-driven techniques. InTech-Open, Chapter 11: 277-308.
Harris, E.L., V. Babovic and R.A. Falconer. 2003. Velocity predictions in compound channels with vegetated floodplains using genetic programming. International Journal of River Basin Management, 1(2): 117-123.
Azamathulla, H.Md. and A. Zahiri. 2012. Flow discharge prediction in compound channels using linear genetic programming. Journal of Hydrology, 454: 203-207.
Kitsikoudis, V. and V. Hrissanthou. 2013. Derivation of sediment transport models for sand bed rivers from data-driven techniques. InTech-Open, Chapter 11: 277-308.
Kitsikoudis, V., E. Sidiropoulos and V. Hrissanthou. 2015. Assessment of sediment transport approaches for sand-bed Rivers by means of machine learning. Hydrological Sciences Journal, 60(9): 1566-1586.
Zahiri, A. and B. Dahanzadeh. 2015. Sediment transport prediction in rivers using quasi-two dimensional model. Journal of Water and Soil Conservation, 22(2): 143-158. (In Persian)
Zahiri, A., F. Hashemi and I. Yousefabadi. 2016. Simulation of two-dimensional velocity distributions in rivers based on Chiu's theory (Case Study: Gorganrood River). Iranian Journal of Eco-Hydrology, 4(3): 791-802. (In Persian)
Munir, S, (2011), Role of sediment transport in operation and maintenance of supply and demand based irrigation canals[D]. Doctoral Thesis, Wageningen, The Netherlands: Wageningen University
Nagy, H.M., Watanabe, K., Hirano, M. (2002). Prediction of Sediment Load concentration in Rivers Using Artifical Neural Network Model.J. Hydraulic Eng.(ASCE),128(6):8-595.
Sivakumar, B., Wallender, W., (2005). Predictability of river flow and suspended sediment transport in the Mississippi River basin: a non-linear deterministic approach. Earth Surf. Process. Landforms Earth Surf. Process. Landforms 30, 665–677.
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Avarideh F, Bani Habib, M and Taher Shamsi, A (2001), Application of Artificial Neural Networks to Estimate River Sediment Flow, Third Iranian Hydraulic Conference, Tehran, University of Tehran, Faculty of Engineering. (In Persian)
Mesbahi, J, Chini, M, (1998), River Engineering Culture, Substantiation Plan of Technical Criteria and Standards of Water Industry, Ministry of Energy. (In Persian)
Asselman, N. E. M. (2000). Fitting and interpretation of sediment rating curves." Journal of Hydrology 234(3–4): 228-248.
Julien.P. Y. Erosion and sedimentation, (2010). Cambridge, UK: Cambridge University Press,
Kisi, O., (2004), Multi-layer perceptrons with Levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol. Sci. J. 49 (6), 1025–1040.
Kisi, O., Yuksel, I., Dogan, E., (2008) Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques. Hydrol. Sci. J. 53 (6), 1270–1285.
Eivani, Z., M.M. Ahmadi and K. Qaderi. 2016. Estimation of suspended sediment load concentration in river system using Group Method of Data Handling (GMDH). Journal of Watershed Management Research, 7(13): 218-229. (In Persian)
Kia, E., A. Emadi and R. Fazlola. 2015. Investigation for application of Adaptive Neuro-Fuzzy Inference System (ANFIS) in Babolroud suspended sediment load estimation. Journal of Watershed Management Research, 6(11): 15-23. (In Persian)
Kitsikoudis, V. and V. Hrissanthou. 2013. Derivation of sediment transport models for sand bed rivers from data-driven techniques. InTech-Open, Chapter 11: 277-308.
Harris, E.L., V. Babovic and R.A. Falconer. 2003. Velocity predictions in compound channels with vegetated floodplains using genetic programming. International Journal of River Basin Management, 1(2): 117-123.
Azamathulla, H.Md. and A. Zahiri. 2012. Flow discharge prediction in compound channels using linear genetic programming. Journal of Hydrology, 454: 203-207.
Kitsikoudis, V. and V. Hrissanthou. 2013. Derivation of sediment transport models for sand bed rivers from data-driven techniques. InTech-Open, Chapter 11: 277-308.
Kitsikoudis, V., E. Sidiropoulos and V. Hrissanthou. 2015. Assessment of sediment transport approaches for sand-bed Rivers by means of machine learning. Hydrological Sciences Journal, 60(9): 1566-1586.
Zahiri, A. and B. Dahanzadeh. 2015. Sediment transport prediction in rivers using quasi-two dimensional model. Journal of Water and Soil Conservation, 22(2): 143-158. (In Persian)
Zahiri, A., F. Hashemi and I. Yousefabadi. 2016. Simulation of two-dimensional velocity distributions in rivers based on Chiu's theory (Case Study: Gorganrood River). Iranian Journal of Eco-Hydrology, 4(3): 791-802. (In Persian)
Munir, S, (2011), Role of sediment transport in operation and maintenance of supply and demand based irrigation canals[D]. Doctoral Thesis, Wageningen, The Netherlands: Wageningen University
Nagy, H.M., Watanabe, K., Hirano, M. (2002). Prediction of Sediment Load concentration in Rivers Using Artifical Neural Network Model.J. Hydraulic Eng.(ASCE),128(6):8-595.
Sivakumar, B., Wallender, W., (2005). Predictability of river flow and suspended sediment transport in the Mississippi River basin: a non-linear deterministic approach. Earth Surf. Process. Landforms Earth Surf. Process. Landforms 30, 665–677.
