استفاده از روش بهینه‌سازی تراکم ذرات در تعیین ضرایب مدل عددی موج کینماتیک- انتشار برای پیش‌بینی جریان ترجیحی آب در خاک

نویسندگان

1 دانش‌آموخته دکتری تخصصی، گروه مهندسی آب دانشگاه شهید چمران اهواز

2 استاد دانشکده مهندسی علوم آب، گروه آبیاری و زهکشی دانشگاه شهید چمران اهواز

3 دانشیار گروه مهندسی آب، دانشکده علوم کشاورزی دانشگاه گیلان و گروه پژوهشی مهندسی آب و محیط زیست، پژوهشکده حوضه آبی دریای خزر، رشت

چکیده

با توجه به حرکت سریع آب و آلاینده از مسیرهای ترجیحی جریان، در این پژوهش از مدل موج کینماتیک- انتشار به­عنوان یکی از راهکارهای مناسب برای شبیه­سازی این حرکت، استفاده شد. این مدل سه ضریب مجهول دارد که با استفاده از روش بهینه­سازی تراکم ذرات تعیین شدند. آزمایش­ها در قالب چهار بارندگی با شدت­های 97/56، 64/107، 01/133 و 71/161 میلی­متر بر ساعت که بر یک ستون خاک می­بارید انجام گرفت و شدت آب خروجی از انتهای ستون خاک در مقابل رطوبت متحرک کل ستون ثبت شد. ضرایب مدل با کمینه کردن تابع خطای بین مقادیر مشاهداتی آزمایش و معادله پیش­بینی شار جریان تعیین شد. برای رسیدن به بهترین جواب و کمینه­ترین مقادیر تابع خطا، راه­حل­های مختلفی ارزیابی شد و مقادیر مختلفی برای c1 و c2 که به­ترتیب ضرایب فردی و اجتماعی الگوریتم بهینه­سازی هستند و در ایجاد نسل­های بعدی پاسخ­های پیشنهادی الگوریتم دخالت دارند، انتخاب و امتحان شد و سرانجام مقادیر 2/1 و 4/2 به­ترتیب برای c1 و c2 منجر به بهترین پاسخ­ها شد. همچنین برای پیدا کردن بهترین پاسخ­ها، معادله­های مختلفی به­عنوان وزن اینرسی، که برای کنترل سرعت حرکت ذرات یا پاسخ­ها در فضای جستجو به­کار می­رود، استفاده شد که سرانجام معادله وزن اینرسی کاهش یابنده خطی که منجر به بهترین پاسخ­ها شد، انتخاب گردید. در کل، نتایج حاکی از توانایی روش بهینه­سازی تراکم ذرات برای تعیین سریع و دقیق ضرایب مدل عددی کینماتیک- انتشار می­باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Utilization of Particle Swarm Optimization Method to Determine Kinematic–Dispersive Wave Model Coefficients for Prediction of the Preferential Water Flow in Soil

نویسندگان [English]

  • M Moradzadeh 1
  • S Boroomandnasab 2
  • H Moazed 3
  • MR Khaledian 3
1 Ph.D. Graduate, Dept. of Water Engineering, Faculty of Water Sciences Engineering, Shahid Chamran Univ. of Ahvaz, Khuzestan, Iran
2 Prof., Faculty of Water Sciences Engineering, Shahid Chamran Univ. of Ahvaz, Khuzestan, Iran
3 Prof., Faculty of Water Sciences Engineering, Shahid Chamran Univ. of Ahvaz, Khuzestan, Iran
چکیده [English]

Due to the rapid movement of water and contaminants through preferential flow paths, in this study kinematic– dispersive wave model as an appropriate method to simulate this motion was used. This model had three unknown coefficients which were determined using particle swarm optimization (PSO) method. Four different rainfall intensities of 56.97, 107.64, 133.01, and 161.71 mm h-1 were applied on the surface of a soil column and output water fluxes from the bottom of the soil column versus the soil mobile moisture amount in the column were recorded. Model coefficients were calculated by minimizing the error function between the observed values and the equation of the flow flux prediction. To achieve the best results and the minimum amount of error function, several solutions were evaluated and different values for c1 and c2 that control the best personal and global, respectively and interfere to make the next generation of results were tested. The best values for c1 and c2 were 1.2 and 2.4, respectively. Also to find the best results, several equations as the inertia weight, to control the particles velositeis in the search spaces, were tested and finally the linear decreasing inertia weight was chosen. Generally the results showed that the used algorithm could define the coefficients of kinematic–dispersive wave model in a short time and with a reasonable accuracy.
 

کلیدواژه‌ها [English]

  • Keywords: Kinematic-dispersive wave model
  • Pollution
  • Porous media
  • Simulation
  • Water

مراجع

Banks A, Vincent J and Anyakoha C, 2007. A review of particle swarm optimization. Part I: background and development. Natural Computing 6: 467-484.
Banks A, Vincent J and Anyakoha C, 2008. A review of particle swarm optimization, Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Natural Computing 7: 109-124.
Di Pietro L, Ruy S and Capowiez Y, 2003. Predicting water flow in soils by traveling-dispersive waves. Journal of Hydrology 278 (1-4): 64-75.
Eberhart RC and Kennedy J, 1995. A new optimizer using particle swarm theory. Pp. 34-43. Proc. 6th Int. Symp. Micro Machine and Human Science, Nagoya, Japan.
Eberhart RC and Shi Y, 2001. Tracking and optimizing dynamic systems with particle swarms. Pp. 94-100. In Evolutionary Computation. Proceedings of the 2001 Congress on Evolutionary Computation,
Feng Y, Teng GF, Wang AX and YaoYM, 2007. Chaotic inertia weight in particle swarm optimization. Pp. 475. Second International Conference on Innovative Computing, Information and Control, Kumamoto, Japan.
Gallage C, Kodikara J and Uchimura T, 2013. Laboratory measurement of hydraulic conductivity functions of two unsaturated sandy soils during drying and wetting processes. Soils and Foundations 53(3): 417-430.
Germann PF, 1985. Kinematic wave approximation to infiltration and drainage into and from soil macropores. Transactions ASAE 28: 745-749.
Germann PF, 1990. Preferential flow and the generation of runoff: boundary layer flow theory. Water Resources Research 26 (12): 3055-3063.
Jamalian, A., Fathali, J., and Nezakati, A. 2010. Location problems with Push-Pull objectyives. M.Sc. thesis, applied mathematic. Shahrood University of technology, faculty of mathematics. (In Persian)
Kennedy J and Eberhart RC, 1995. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, IEEE Service Center, Piscataway 4: 1942-1948.
Kentzoglanakis K and Poole M, 2009. Particle swarm optimization with an oscillating inertia weight. Pp. 1749-1750. Proceedings of the 11th Annual conference on Genetic and evolutionary computation, ACM.
Li HR and Gao YL. 2009. Particle swarm optimization algorithm with exponent decreasing inertia weight and stochastic mutation. Pp. 66-69. Second International Conference on Information and Computing Science, Hyderabad, India.
Majdalani M, Angulo-Jaramillo R and Di Pietro L, 2008. Estimating preferential water flow parameters using a binary genetic algorithm inverse method. Environmental Modelling & Software 23: 950-956.
Malik RF, Rahman TA, Hashim SZM, and Ngah R, 2007. New particle swarm optimizer with sigmoid increasing inertia weight. International Journal of Computer Science and Security (IJCSS) 1(2): 35.
Merrikh Bayat, F. 2012. Optimization algorithms inspired by nature. Nas Press. (In Persian)
Moussa R, 1997. Geomorphological transfer function calculated from digital elevation models for distributed hydrological modelling. Hydrological Processes 11: 429-449.
Nielsen DR and Biggar YW, 1961. Measuring capillary conductivity. Soil Science 92: 192-193.
Nikabadi A and Ebadzadeh M, 2008. Particle swarm optimization algorithms with adaptive Inertia Weight: A survey of the state of the art and a Novel method. IEEE Journal of Evolutionary Computation.
Poulovassilis A, 1969. The effect of hysteresis of pore-water on the hydraulic conductivity. Journal of Soil Science 20(1): 52-56.
Ramos E, Storey BD, Sierra F, Zuniga RA and Avramenko A, 2004.Temperature distribution in an oscillatory flow with a sinusoidal wall temperature. International Journal of Heat and Mass Transfer 47: 4929-4938.
Rauch W and Harremoës P, 1999. On the potential of genetic algorithms in urban drainage modeling. Urban Water 1(1): 79-89.
Rousseau M, Ruy S, Di Pietro L and Angulo-Jaramillo R, 2004. Unsaturated hydraulic conductivity of structured soils from a kinematic wave approach. Journal of Hydraulic Research 42:83-91.
Shi Y and Eberhart R, 1998. A modified particle swarm optimizer. Pp. 69-73. Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence. Anchorage, USA.
Van Genuchten MTh, 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal 44(5): 892-898.
Wang Y, Bradford SA and Šimůnek J, 2013. Transport and fate of microorganisms in soils with preferential flow under different solution chemistry conditions. Water Resources Research 49(5): 2424–2436.
Wang Y, Bradford SA and Šimůnek J, 2014. Physicochemical factors influencing the preferential transport of Escherichia coli in soils. Vadose Zone Journal 13(1): 1-10.
Xin J, Chen G and Hai Y, 2009. A particle swarm optimizer with multistage linearly-decreasing inertia weight. Pp. 505-508. International Joint Conference on Computational Sciences and Optimization. Sanya, Hainan, China.
Youngs EG, 1964. An infiltration method of measuring the hydraulic conductivity of unsaturated porous materials. Soil Science 97(5): 307-311.
دانش آب و خاک
دوره 28، شماره 3
مهر 1397
صفحه 1-12

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