تحلیل عدم قطعیت در برنامه‌ریزی توزیع آب شبکه آبیاری میان‌آب دشت شوشتر: کاربرد الگوریتم ژنتیک و شبیه سازی فرایند سرد شدن فلزات

نوع مقاله: مقاله پژوهشی

نویسنده

استادیار دانشگاه آزاداسلامی، واحد شوشتر، گروه مهندسی علوم آب، شوشتر، ایران

چکیده

آب دریافتی محصولات کشاورزی در یک الگوی کشت در اراضی و فصول مختلف با تغییرات زیادی مواجه می­گردد. از این جهت کاشت هر محصول با توجه به شرایط اقلیمی، تنش خشکی، بارندگی و حساسیت گیاهی با سطوح متفاوتی از عدم قطعیت و ریسک­ همراه است. در این مطالعه نقش نوسانات زمانی آب تخصیص داده شده برای محصولات عمده الگوی کشت شبکه آبیاری میان­آب دشت شوشتر بررسی شده است. مدل شبیه­سازی الگوی کشت با هدف بیشینه­سازی درآمد خالص با محدودیت­های آبیاری، سرمایه­گذاری، گیاهی و زمین توسط روش شبیه­سازی فرایند سرد شدن فلزات بهینه­سازی شده است. پاسخ­های به­دست آمده در شرایط عدم قطعیت برای تعیین اثر نوسانات تنش خشکی به هر یک از محصولات در فرایند تحلیل فازی قرار گرفته است. تئوری فازی با تابع عضویت مثلثی تعریف و در پنج سطح آلفا برابر با 0، 25/0، 5/0، 75/0 و 1 برای یافتن پاسخ فازی مسآله به کار گرفته شده است. در هر سطح مثبت و منفی آلفا برای جستجوی پاسخ­های مرزی از روش الگوریتم ژنتیک استفاده شده است. نتایج نشان داد کاربرد برنامه بهینه موجب کاهش سالانه 7 میلیون متر مکعب در تخصیص آب و افزایش بیش از پنج میلیارد تومان در سود خالص کل الگوی دشت می­گردد. تحلیل فازی سیستم توسعه داده شده نشان داد با کاهش 25 درصدی آبیاری بهینه، بهره­وری اقتصادی آب حداقل 30 درصد افزایش خواهد یافت.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Uncertainty Analysis of Water Distribution Planning in Mian-Ab Irrigation Network in Shooshtar Plain: Application of Genetic Algorithm and Simulated Annealing

نویسنده [English]

  • Saeb Khoshnavaz
Department of water science, shoushtar branch, Islamic Azad university, Shoushtar, Iran
چکیده [English]

Allocated water for agricultural crops in a cropping pattern in different regions and seasons is faced with much variation. Therefore, the cultivation of each crop is subject to climatic conditions, drought tension, precipitation, and crop sensitivity with different levels of uncertainty and risk. In this study, the role of allocated water and its time fluctuations for the main crops in Mian-Ab irrigation network in Shooshtar plain have been investigated. The simulation model of cropping pattern with the objective of maximizing net income with irrigation, investment, cultivation, and land constraints was optimized using a simulated annealing algorithm. The obtained responses in the uncertainty conditions will determine the effect of tension fluctuations in fuzzy analysis process. Fuzzy set theory has been defined with triangular membership function and has been divided into five alpha levels of 0, 0.25, 0.5, 0.75, and 1 to find the fuzzy response of the problem. In each positive or negative α level, a genetic algorithm sub-model has been used with a proximity criterion to find the boundary responses. The results showed that the application of optimal strategy reduced water consumption up to 7 MCM/year and increased the net benefit in cropping pattern more than 5×1010 IRR annually. The developed fuzzy model showed that the water efficiency will be increased at least 30% with a 25% reduction in optimal irrigation..

کلیدواژه‌ها [English]

  • Cropping pattern
  • Genetic Algorithm
  • Mian-Ab irrigation network
Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1998). Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper, no 56. Rome, Italy.

Anwar, A.A. and Haq, Z.U. (2012). Genetic algorithms for the sequential irrigation scheduling problem. Irrigation Science. DOI 10.1007/s00271-012-0364-y.

Asadpour, H. Khalilian, S and Paekani, G.R. (2004). Theory and application of fuzzy Armani linner programming model in Cropping Pattern optimization. Agricultural Economic and Development. 338-307. (In Farsi)

Azarafza, H. Rezaei, H. Behmanesh, J. and Besharat S. (2012) Results Comparison of Employing PSO, GA and SA Algorithms in Optimizing Reservoir Operation (Case Study: Shaharchai Dam, Urmia, Iran). Journal of Water and Soil, 26(5), 1101-1108. (In Farsi)

Babazadeh, H. Eftekhar, Sh. and Sedghi, H. (2011). Irrigation Scheduling optimization with using of Genetic alghorithm(Case Study: Ghazvin plain). Journal of Water Research in Agriculture, 25(2):183-194. (In Farsi)

Dandy, G.C., Engelhardt, (2001). The optimal scheduling of water main replacement using genetic algorithm. Journal of Water Resource Planning and Management. ASCE, 127(4): 214-223.

Doorenbos, J. and Kassam, A.H. (1979). Yield Response to Water. FAO Irrigation and Drainage paper No. 33, FAO, Rome, Italy, p. 193.

FAO. (1992). CROPWAT, a Computer Program for Irrigation Planning and Management by M. Smith. FAO Irrigation and Drainage Paper No. 26. Rome.

FAO. (2012). Crop yield response to water by P. Steduto, T.C. Hsiao, E. Fereres, and D. Raes. FAO Irrigation and Drainage Paper No. 66. Rome.

Ghadami, S. M. Ghahraman, B. M. Sharifi, B. and Rajabi Mashhadi, H.(2009). Optimization of Multireservoir Water Resources Systems Operation Using Genetic Algorithm. Journal of Iran-Water Resources Research,5(2):1-15. (In Farsi)

Goldberg, D.E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, Mass. Pp: 399.

Gurav, J.B. and Regulwar, D.G. (2012). Multi objective sustainable irrigation planning with decision parameters and decision variables fuzzy in nature. Water resource management. 26:3005-3021.

Haghighi, A., Zahedi-Asl, A. 2014. Uncertainty analysis of water supply networks using the fuzzy set theory and NSGA-II. Eng. App. Art. Int. 32, 270–282.

Heydari, N., Eslami, A., Ghadami, A., Kanoni, A., Asadi, M.E. and Khajehabdollahi, M.H. (2005). Determination and evaluation of agricultural water use efficiency (WUE) in Iran. International Agricultural Engineering Conference, Asian Institute of Technology (AIT), Bangkok, Thailand.

Janova, J. (2012). Crop planning optimization model: the validation and verification processes. Central European Journal of Operations Research. 20:451-462.

Khashei, A., Ghahraman, B. and Kouchakzadeh, M. 2013. Application of Allocation and Agricultural Water Management Using Particle Swarm Optimization (Case study: Neishabour Plain). Journal of Water and Soil. 27(2): 292-303. (In Farsi)

Khashei, A., Shahidi, A., pourrezabilondi,M., Amirabadizadeh, M. and Jafarzadeh  A. (2018).Performance Assessment of ANN and SVR for downscaling of daily rainfall in dry regions.Iranian Journal of Soil and Water Research.49(4):781-793

Kirkpatrick, S., Gelatt C.D. and Vecchi M.P. (1982). Optimization by Simulated Annealing. Science. 220(4598): 671-680.

Kohansal, M. and Hamraz, S. S. (2008) Drought management in Agriculture with using of optimum Cropping Pattern based on Fuzzy logic(case study: Taibad plain).In: Proceedings of 1th International Congress on  Water Crisis, 10-12 Mar., Zabol university, Iran, pp. 140-153. (In Farsi)

Lalehzari, R., Boroomand-Nasab, S., Moazed, H., and Haghighi, A. (2016). Multi-objective management of water allocation to sustainable irrigation planning and optimal cropping pattern. J. Irri. Drain. Eng. 142(1): 05015008.

Lalehzari, R., Moazed, H., Boroomand Nasab, S. and Haghighi, A. 2015. Development of Mathematical and Optimization Model for Agricultural Water Allocation Based on Non-dominated Sorting. Water and Soil Resources Conservation Journal. 5(1): 17-30. (In Farsi)

Lu, H.W., Huang, G.H. and He, L. (2010). Development of an interval-valued fuzzy linear programming method based on infinite α-cuts for water resources management. Environmental Modelling and Software. 25(3): 354-361.

Moghaddasi, M. Morid, S. and Araghinejad, S. (2008) Optimization of Water Allocation During Water Scarcity Condition Using Non-Linear Programming, Genetic Algorithm and Particle Swarm Optimization (Case Study). Journal of Iran-Water Resources Research, 4(3):1-13. (In Farsi)

Mohammadi, H. Bostani, F. and Kafil zadeh, F. (2013). Determination of optimum Cropping Pattern using of Fuzzy nonliner multiobjective optimization algorithm (Case Study). Journal of water and wastewater.4:43-55. (In Farsi)

Montazar, A. (2013). A decision tool for optimal irrigated crop planning and water resources sustainability. Journal of Global Optimization. 55:641–654.

Moradi-Sabzkouhi, A., Haghighi, A. (2016). Uncertainty Analysis is of pipe-network hydraulics using a many-objective particle swarm optimization. J. Hyd. Eng. 142(9), 04016030.

Mortazavi, A. Azdari, S. and Mousavi, S. H. (2012). Determination of optimum Cropping Pattern and marketing in uncertainty situation (case study: Arzan,Fars). Journal of Agriculture Economy,5(3):75-94. (In Farsi)

Nimah, M.N., Bsaibes, A., Alkahl, F., Darwish, M.R. and Bashour, I. (2003). Optimizing cropping pattern to maximize water productivity. Published in river Basinn Management. Pp: 187-197.

Raju, K.S. and Kumar, D.N. (2004). Irrigation planning using genetic algorithm. Water Resource Management. 18:163-176.

Ramezani, H., Liaghat, A., Parsinejad, M., Tavakoli,A.R. and Bozorg-Hadad, O.S. (2012). Development of an Optimization Model for Water Allocation in Irrigated and Rainfed Lands to increase Economical Productivity. Ph.D. Thesis. University of Tehran. Pp:170. (In Farsi)

Reddy, J.M., and Kumar, N.D. (2008). Evolving Strategies for Crop Planning and Operation of Irrigation Reservoir System using Multi-Objective Differential Evolution. Irrigation Science, 26(2): 177-190.

Regulwar, D.G. and Gurav, J.B. (2012). Sustainable irrigation planning with imprecise parameters under fuzzy environment. Water Resource Management. 26:3871–3892.

Saboohi, M. and Alvanchi, M. (2008). Application of Multi Objective and Compromise Programming to Farm Planning: A Case Study of Mashhad plain. J. Agric. Sci. Natur. Resour.,15(3): 292-303. (In Farsi)

Singh, A. and Panda, S.N. (2012). Development and application of an optimization model for the maximization of net agricultural return. Agricultural Water Management. 115:267-275.

Singh, D.K., Jaiswal, C.S., Reddy, K.S., Singh, R.M., Bhandarkar, D.M. (2001). Optimal cropping pattern in a canal command area. Agricultural Water Management 50 (1): 1-8.

Solis, F.J., Gonzalez-Sanchez, A.G. and Larre, M. (2009). A new method for optimal cropping pattern. MICAI: Advances in Artificial Intelligence. 5845:566-577.

Steduto, P., T.C. Hsiao, D. Raes, and E. Fereres. (2009). AquaCrop—Th e FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J. 101:426–437.

Tavakoli, A. R. Liaghat, A. and Alizadeh, A. (2014). Soil Water Balance, Sowing Date and Wheat Yield Using AquaCrop Mode under Rainfed and Limited Irrigation. Journal of Agricultural Engineering Research 14(4):41-56

Toyonaga, T., Itoh, T. (2005). A Crop Planning Problem with Fuzzy Random Profit Coefficients. Fuzzy Optimization and Decision Making, 4, 51–69.

Vedula, S., Mujumdar, P.P. and Sekhar, G.C. (2005). Conjunctive use modeling for multicrop irrigation. Agricultural Water Management. 73:193-221.

Wardlaw, R. and Bhaktikul, K. (2004). Application of genetic algorithms for irrigation water scheduling. Irrigation and Drainage. 53(4): 397-414.

Zhang, B., Yuan, S., Zhang, J.S. and Li, H. (2008). Study of Corn Optimization Irrigation Model by Genetic Algorithms. Computer and Computing Technologies in Agriculture. 258:121-132.