بهینه‌سازی ضرایب معادله منحنی سنجه رسوب در برآورد دبی رسوب با استفاده از الگوریتم ازدحام ذرات (PSO) و الگوریتم تبرید (SA) (مطالعه موردی ایستگاه شهر بیجار)
محورهای موضوعی :
برآورد رسوب
علیرضا وفایی نژاد
1
,
زهرا چترسیماب
2
,
سمیرا بلوری
3
,
فرشاد میردار هریجانی
4
1 - استادیار دانشکده مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی
2 - دانشجوی دکترای رشته سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه آزاد اسلامی واحد علوم و تحقیقات،
3 - دانشجوی دکترای رشته سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه آزاد اسلامی واحد علوم و تحقیقات
4 - کارشناس ارشد آبخیزداری، سازمان جنگلها، مراتع و آبخیزداری کشور
تاریخ دریافت : 1395/11/02
تاریخ پذیرش : 1396/07/09
تاریخ انتشار : 1396/09/01
کلید واژه:
SA,
بهینه‌سازی,
الگوریتم PSO,
رسوبات معلق,
ایستگاه شهربیجار,
چکیده مقاله :
پدیده فرسایش و انتقال رسوب در رودخانه‌ها یکی از مهمترین و پیچیده‌ترین موضوعات می‌باشد. این پدیده‌ها اثرات ویژه‌ای روی شاخص‌های کیفی آب، کنش کف بستر و کناره‌های رودخانه داشته و همچنین خسارات جبران ناپذیری به طرح‌های عمرانی وارد می‌نماید. برای تخمین هر چه بهتر میزان رسوب معلق بر اساس معادله منحنی سنجه می‌توان ضرایب این معادله را بهینه نمود. یکی از روشهای بهینه‌سازی ضرایب معادله منحنی سنجه رسوب، استفاده از الگوریتمهای فراابتکاری می‌باشد. هدف اصلی این تحقیق استفاده از الگوریتم بهینه‌سازی ازدحام ذرات و تبرید برای بهینه کردن ضرایب معادله منحنی سنجه رسوب برای ایستگاه شهر بیجار و مقایسه نتایج بدست آمده از این مدل‌ها با منحنی سنجه رسوب می‌باشد. برای محاسبه دبی رسوب ، آمار و اطلاعات لازم از جمله آمار دبی آب و غلظت اندازه‌گیری شده رسوب در ایستگاه مورد مطالعه جمع‌آوری شده است. مدل‌های الگوریتم بهینه‌سازی ذرات و تبرید در نرم‌افزار متلب کدنویسی شد. پس از اینکه مدل‌ها با 70 درصد از داده‌ها مورد آموزش قرار گرفت، 15 درصد داده‌ها در ایستگاه شهر بیجار مورد آزمون قرار گرفت. معیار ارزیابی مدل‌ها ضریب تبیین ، ضریب نش ستکلیف و جذر میانگین مربعات خطا بوده است. نتایج بدست آمده از مدل‌ها که در واقع کمینه کردن خطای حاصل از داده‌های محاسبه شده و مقادیر واقعی می‌باشد نشان دهنده این واقعیت است که مدل الگوریتم بهینه‌سازی ازدحام ذرات با مقدار 6.6 تن در روز در ایستگاه شهر بیجار دارای کمترین مقدار جذر میانگین مربعات خطا و پس از آن، الگوریتم تبرید با مقدار 19.7 تن در روز قرار می‌گیرد.
چکیده انگلیسی:
Erosion and sediment transport in rivers is one of the most important and complex issue. This special effects on water quality indices, floor and sides of the river action and also damages the development projects inserted to better estimate the suspended sediment rating curve equation is based on the equation coefficients be optimized. One of the sediment rating curve coefficients optimization methods, the use of met heuristic respectively. This study optimization algorithm and particle swarm optimization equipment for sediment rating curve coefficients for the stations and Bijar The results of these models with sediment rating curves respectively. To calculate sediment discharge, required data such as water discharge and sediment concentration measured at study stations is collected Algorithm optimization models particles and refrigeration were coded in MATLAB software. After the models were trained with 70 percent of the data, 15 percent of the data was tested in Bijar station. Standard models to evaluate the coefficient of determination, Nash coefficient and root mean square error, respectively. Minimum amount of root mean square error and then annealing the lie to the amount of 19.7 tons per day.
منابع و مأخذ:
References:
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Caretagena, D.F. (2004) Remotely sensed land cover parameter extraction for watershed erosion modeling. MS.c thesis, International institute for geo-information science and earth observation, Enschede, The Netherland, ITC publications.
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Malava, J. and F, Bonda. (1999) Proposal for research to support erosion hazard assessment in Malawi. Agricultureal engineering Bunda College of Agriculture, www.ag.arizona.edu.
Misevicius A. (2003) A Modified simulated annealing algorithm for the quadratic assignment problem informatica. 14: 497-514.
Mohammad Reza Pour, O., Lee, T. SH. and et. al. (2011) Genetic algorithm model for the relation between flow discharge and suspended sediment load (Gorgan River in Iran). Electronic journal of geotechnical engineering, 16:539-555.
Mohammad Reza Pour, O., Haghighatjou, P. and et. al. (2015) Analogy between PSO and genetic algorithm in optimizing coefficient of in estimating of suspended erosion debye in sistan river: case study of Kahak station. Journal of watershed and water engineering, 26.
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.
Pereze, R. and K. Behdinan. (2007) Particle swarm approach for structural design optimization. Computers and Structures, 85:1579-1588.
Rostaei, S., Nikjou, MR. and et. al. (2011) Investigation of Soil erodibility of Bejoshan Chay Basin based on Fuzzy-GIS. Journal of Geography and planning, Tabriz University, 33:147-173.
Tran, N. H., Chen, Z. and et. al. (2003) Object-based global optimization in modeling discrete-fracture network map: A case study SPE 84456. Annual technical conference and exhibition, Denver, Colorado, U. S. A.
Vasan, A. and K. S, Raju. (2009) Comparative analysis of simulated annealing. Simulated quenching and genetic algorithms for optimal reservoir operation Elsevier. Applied soft computing, 9:274-281.
Altunkaynak.A. 2009. Sediment load prediction by genetic algorithms. J. Adv.Engineering Software.40: 928-934.
Babovic V, Keijzer M, Aguilera, DR, Harrington J. (2001). Automatic Discovery Settling Velocity Equations. D2K Technical Report, D2K-0201-1.
S_en Z, Oztopal A. Genetic algorithms for the classification and prediction of precipitation occurrence. Hydrol Sci J 2001;46(2):255–68.
_||_References:
Abdi Dehkordi, M., Meftah, M., Dehghani, A. and M. Hesam. (2010) Application of genetic algorithm in optimizing coefficients of function. 5th congress of watershed and management of soil and water resources.
Ale Sheikh, A., Gazmeh, H., Ghehreghan, A. and M, Karimi. (2013) Modeling the spread of forest fire using cellular automata, GIS and bird’s algorithm. journal of surveying engineering and spatial information, 4(3).
Amini, F. and H. Baghi. (2010) Active control of buildings using reformed bird’s algorithm. 6th national congress of civil engineering, Semnan university, Iran.
Baizaei, M., Erfanian, M., Abghari, H, and A. Esmaeili. (2010) Evaluation of methods in estimation of river suspend burdens in Azarbaijane sharghi. 7th national congress of science and watershed engineering, 27,28 th April.
Caretagena, D.F. (2004) Remotely sensed land cover parameter extraction for watershed erosion modeling. MS.c thesis, International institute for geo-information science and earth observation, Enschede, The Netherland, ITC publications.
Clerc, M. and J. kennedy. (2002) The Particle Swarm-Explosion Stability, and Convergence in a Multidimensional Complex Space. IEE Transactions on Evolutionary Computation, 58-73.
Dawson, C. W. and R. L.Wilby. (2001) Hydrological modeling using artificial neural networks. Progress in Physical Geography, 108:25-80.
Durand, M. D. and S. R.White. (2000) Trading accuracy for speed in parallel simulated annealing with simultaneous moves. Elsevier parallel computing, 26:135-150.
Eberhart, R. C. and Y. Shi. (2001) Comparing inertia weights and constriction factor in Particle Swarm Optimization. In Proceedings of the Congress on Evolutionary Computation, 84-88.
Gholinezhad, S. and M. Masihi. (2012) Conformity and speedy simulated annealing algorithm for modeling break network in natural broken depository. Oil research, 80.
Hudson, N. (1981) Soil Conservation. The University of Michigan,Cornell University Press, 324.
Karaboga ,D. and B, Basturk. (2008) On the performance of artificial bee colony (ABC) algorithm. Applied Soft computing. 8:687-697.
Kennedy, J., Eberhart, C. and et.al. (1995) Particle swarm optimization. In Proceedings of IEEE international conference on neural network, Perth, Australia, 4:1942-1948.
Malava, J. and F, Bonda. (1999) Proposal for research to support erosion hazard assessment in Malawi. Agricultureal engineering Bunda College of Agriculture, www.ag.arizona.edu.
Misevicius A. (2003) A Modified simulated annealing algorithm for the quadratic assignment problem informatica. 14: 497-514.
Mohammad Reza Pour, O., Lee, T. SH. and et. al. (2011) Genetic algorithm model for the relation between flow discharge and suspended sediment load (Gorgan River in Iran). Electronic journal of geotechnical engineering, 16:539-555.
Mohammad Reza Pour, O., Haghighatjou, P. and et. al. (2015) Analogy between PSO and genetic algorithm in optimizing coefficient of in estimating of suspended erosion debye in sistan river: case study of Kahak station. Journal of watershed and water engineering, 26.
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.
Pereze, R. and K. Behdinan. (2007) Particle swarm approach for structural design optimization. Computers and Structures, 85:1579-1588.
Rostaei, S., Nikjou, MR. and et. al. (2011) Investigation of Soil erodibility of Bejoshan Chay Basin based on Fuzzy-GIS. Journal of Geography and planning, Tabriz University, 33:147-173.
Tran, N. H., Chen, Z. and et. al. (2003) Object-based global optimization in modeling discrete-fracture network map: A case study SPE 84456. Annual technical conference and exhibition, Denver, Colorado, U. S. A.
Vasan, A. and K. S, Raju. (2009) Comparative analysis of simulated annealing. Simulated quenching and genetic algorithms for optimal reservoir operation Elsevier. Applied soft computing, 9:274-281.
Altunkaynak.A. 2009. Sediment load prediction by genetic algorithms. J. Adv.Engineering Software.40: 928-934.
Babovic V, Keijzer M, Aguilera, DR, Harrington J. (2001). Automatic Discovery Settling Velocity Equations. D2K Technical Report, D2K-0201-1.
S_en Z, Oztopal A. Genetic algorithms for the classification and prediction of precipitation occurrence. Hydrol Sci J 2001;46(2):255–68.
