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تخصیص عادلانه منابع آب با کاربرد تئوری آنتروپی شانون در روش برنامهریزی سازشی | ||
| تحقیقات آب و خاک ایران | ||
| مقاله 4، دوره 50، شماره 2، خرداد و تیر 1398، صفحه 297-312 اصل مقاله (1.23 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2018.258049.667913 | ||
| نویسندگان | ||
| رضا ایوبی کیا1؛ سمیه جنت رستمی* 2؛ افشین اشرف زاده3؛ بهنام شفیعی ثابت4 | ||
| 1دانش آموخته کارشناسی ارشد مهندسی منابع آب، دانشگاه گیلان، رشت، ایران. | ||
| 2گروه مهندسی آب، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، گیلان. | ||
| 3دانشیار گروه مهندسی آب دانشگاه گیلان | ||
| 4استادیار گروه مهندسی آب، دانشگاه گیلان، رشت، ایران. | ||
| چکیده | ||
| با توجه به تأثیر قابل‌ملاحظه آب در توسعه اقتصادی، اجتماعی و تعادل زیستمحیطی، تخصیص منابع آب به یک مسئله جهانی تبدیل شده است. در این مطالعه، یک مدل برنامهریزی تخصیص آب چندهدفه در حوضه آبریز سفیدرود ارائه گردید که شامل دو هدف حداکثر کردن بهرهوری سود اقتصادی و عدالت در تخصیص آب است. برای حل مدل توسعه‌یافته و ایجاد برهمکنش مناسب بین دو هدف بهرهوری سود و عدالت از روش برنامهریزی سازشی استفاده شد. وزنهای مختلف توابع هدف، به همراه تعریف طرحهای TDS، DSA و DSB به ترتیب با نگرش تعادلی، بهرهوری سود اقتصادی و برقراری عدالت بررسی شد که نتایج نشان داد طرح TDS، بهترین طرح از دیدگاه برقراری تعادل بین توابع هدف است. به‌استثنای TDS، نتایج نشان داد که مقادیر تخصیص آب سطحی و سود اقتصادی در سایر وزنهای توابع هدف از روند خاصی پیروی نمیکند، به‌طوری‌که برنامهریز در انتخاب بهترین وزن توابع هدف دچار مشکل میشود. استفاده از تئوری آنتروپی شانون راهحل مناسبی برای انتخاب بهترین وزنهای توابع هدف است. نتایج حاصل از کاربرد این تئوری در روش برنامهریزی سازشی نشان داد که بهترین جواب با در نظر گرفتن اولویت برنامهریزان منطقه با استفاده از وزنهای 35/0 برای هدف بهرهوری سود و 65/0 برای هدف عدالت تخصیص بدست میآید. به‌طورکلی نتایج حاصل از این مطالعه نشان داد در شرایطی که اولویتهای برنامهریزان آب در منطقه مشخص نباشد، میتوان هم‌زمان با کاربرد روش برنامهریزی سازشی برای حل مسائل بهینهسازی چندهدفه از تئوری آنتروپی شانون برای تعیین وزن هر یک از توابع هدف استفاده نمود تا تعادلی بین توابع هدف برقرار شود. | ||
| کلیدواژه‌ها | ||
| بهرهوری؛ عدالت؛ منابع آب؛ مدیریت؛ حوضه آبریز سفیدرود | ||
| عنوان مقاله [English] | ||
| Equitable Allocation of Water Resources Using Shannon Entropy Theory in Compromise Programming Method | ||
| نویسندگان [English] | ||
| Reza Ayoubi kia1؛ Somaye Janatrostami2؛ Afshin Ashrafzadeh3؛ Behnam Shafiei-sabet4 | ||
| 1Department of Water Engineering, College of Agriculture, University of Guilan, Rasht. | ||
| 2Department of Water Engineering, College of Agriculture, University of Guilan, Rasht, Guilan. | ||
| 3Associate Professor, Department of Water Engineering, College of Agriculture, University of Guilan, Rasht, Iran. | ||
| 4Assistant Professor, Department of Water Engineering, College of Agriculture, University of Guilan, Rasht. | ||
| چکیده [English] | ||
| Because of the significant impact of water on economic development, social stability and ecological balance, the allocation of water resources has become a worldwide issue. In this paper, a multi-objective planning model consist of two objective functions was developed for water allocation to maximize the productivity of economical benefit and the equity of water allocation in Sefidroud basin located in Iran. A Compromise Programming method was applied to trade off both the objective functions. The different weights of the objective functions and the definitions of TDS, DSA and DSB schemes were investigated in terms of equity, economical benefit productivity and equity establishment. The results showed that TDS is the best scheme for balancing the target functions. With the except of TDS, surface water allocation and economical benefit do not follow a particular pattern in other weights of target functions, so that decision makers are confused to choose the best weights of the objective functions. Shannon Entropy theory is a suitable solution for selecting the best weights of the objective functions. The results obtained by applying the Shannon Entropy theory showed that the best weights of the objective functions for the productivity of economical benefit and water allocation equity according to the decision maker’s priority were 0.35 and 0.65, respectively. Generally, the results of this study showed that if decision maker’s priorities were not clear, Shannon Entropy theory and Compromise Programming method could be used to determine the weights of each objective functions in order to make a balance among the objective functions. | ||
| کلیدواژه‌ها [English] | ||
| Efficiency, Equity, water resources, management, Sefidroud basin | ||
| مراجع | ||
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Akhooni Pourhosseini F. and Ghorbani, M. A. (2017). Application of Shannon Entropy in Determining the Most Effective Chemical Parameter in Surface Water Quality (Case Study: Sofi Chay Watershed). Journal of Environmental Water Engineering, 2(4), 322 -332. (In Farsi) Babel, M.S. Gupta, A. D. and Nayak, D.K. (2005). A Model for Optimal Allocation of Waterto Competing Demands. Water resources management, 19(6), 693-712. Cullis, J. and Koppen, B.V. (2007). Applying the Gini Coefficient to measure inequality of water use in the Olifants river water management area, South Africa. International Water Management Institute, Report 113. Dai, C. Qin, X.S. Chen, Y. and Guo, H.C. (2018). Dealing with equality and benefit for water allocation in a lake watershed: A Gini-coefficient based stochastic optimization approach. Journal of Hydrology, 561, 322-334. Dwaf, A. (2005). White Paper on a National Water Policy for South Africa. Department of Water Affairs and Forestry, Pretoria. Fattahi, P. and Fayyaz, S. (2010). A compromise programming model to integrated urban water management. Water Resources Management, 24(6), 1211–1227. Gini, C. (1921). Measurement of inequality of incomes. The Economic Journal, 31(121), 124–126. Han, Y. Huang, Y.F. Wang, G.Q. and Maqsood, I. (2011). A multi-objective linear programming model with interval parameters for water resources allocation in Dalian city. Water Resources Management, 25, 449–463. Higgins, A. Archer, A. and Hajkowicz, S. (2008). A stochastic non-linear programming model for a multi-period water resource allocation with multiple objectives. Water Resources Management, 22(10), 1445–1460. Hu, Z. Chen, Y. Yao, L. Wei, C. and Li, C. (2016). Optimal allocation of regional water resources: From aperspective of equity–efficiency trade off. Resources, Conservation and Recycling, 109, 102–113. Iftekhar, M.S. and Fogarty, J. (2017). Impact of water allocation strategies to manage groundwater resources in Western Australia: Equity and efficiency considerations. Journal of Hydrology, 548, 145-156. Mimi, Z. and Sawalhi, B.I. (2003). A decision tool for allocating the waters of the Jordan river basin between all riparian parties. Water Resources Management, 17, 447–461. Monghasemi, S. Nikoo, M.R. Khaksar Fasaee, M.A. and Adamowski, J. (2015). A novel multi criteria decision making model for optimizing time-cost-quality trade-off problems in construction projects. Expert Systems with Applications, 42(6),3089–3104. Neumayer, E. (2011). Sustainability and inequality in human development. UNDP-HDRO Occasional Papers Vol (4). New York. Roozbahani, R. Abbasi, B. and Schreider, S. (2015a). Optimal allocation of water to competing stakeholders in a shared watershed. Annals of Operations Research, 229(1), 657–676. Roozbahani, R. Schreider, S. and Abbasi, B. (2015b). Optimal water allocation through a multi-objective compromise between environmental, social, and economic preferences. Environmental Modelling & Software, 64, 18–30. Seekell, D.A. D'Odorico, P. Pace, M.L. and Dodorico, P. (2011). Virtual water transfers unlikely to redress inequality in global water use. Environmental Research Letters, 6(2), 024017. Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27, 379- 423. Smakhtin, V. U. (2001). Low flow hydrology: a review. Journal of Hydrology, 240, 147-186. Sun, T. Zhang, H. and Wang, Y. (2013). The application of information entropy in basin level water waste permits allocation in China. Resources, Conservation and Recycling, 70, 50-54. Syme, G. J. Nancarrow, B. E. and McCreddin, J. A. (1999). Defining the components of fairness in the allocation of water to environmental and human uses. Journal of Environmental Management, 57(1), 51–70. Tennant, D. L. (1976). In stream flow regimes for Fish, wildlife, recreation and related environmental resources. Fisheries, 1, 6-10. Tsur, Y. and Dinar, A. (1995). Efficiency and Equity Considerations in Pricing andAllocating Irrigation Water. policy Research Working Paper (vol.1). (pp. 37-40). Vpsps. (2011). Guideline for finding aquatic ecosystems environmental water requirement. Vice Presidency for Strategic Planning and Supervision, 557, 127 p. (In Farsi) Wang, E. Alp, N. Shi, J. Wang, C. Zhang, X. and Chen, H. (2017). Multi-criteria building energy erformance benchmarking through variable clustering based compromise TOPSIS with objective entropy weighting. Energy, 125, 197-210. Wang, X. Zhang, J. Shahid, S. ElMahdi, A. He, R. Wang, X. and Ali, M. (2012). Gini coefficient to assess equity in domestic water supply in the Yellow River. Mitigation and Adaptation Strategies for Global Change, 17, 65–75. Wang, Y. D. Lee, J. S. Agbemabiese, L. Zame, K. and Kang, S. G. (2015). Virtual water management and the water–energy nexus: a case study of three Mid-Atlantic states. Resources, Conservation and Recycling, 98, 76–84. Xavier, A. Freitas, M.B.S. Fragoso, R. and Rosário, M.S. (2018). A regional composite indicator for analysing agricultural sustainability in Portugal: A goal programming approach. Ecological Indicators, 89, 84-100. Young, H.P. (1994). Equity: in theory and practice. Princeton University Press, 238p. Yuan, Q. McIntyre, N. Wu, Y. Liu, Y. and Liu, Y. (2017). Towards greater socio-economic equality in allocation of wastewater discharge permits in China based on the weighted Gini coefficient. Resources, Conservation and Recycling, 127, 196-205. Yue, L. P. Hui, Q. and Hua, W. (2010). Groundwater Quality Assessment Based on Improved Water Quality Index in Pengyang County, Ningxia, Northwest China. Journal of Chemistry, 7(1), 209-216. Zarghami, M. Abrishamchi, A. and Ardakanian, R. (2008). Multi-criteria decision making for integrated urban water management. Water Resources Management, 22(8), 1017–1029. Zarghami, M. and Szidarovszky, F. (2010). On the relation between compromise programming and the ordered weighted averaging operator. Information Sciences, 180(11), 2239-2248. Zeleny, M. (1973). Compromise programming. In: Multiple Criteria Decision Making. University of South Carolina Press, Columbia, 263–301. Zhang, Z. and Shao, Y. (2010). Inequality and polarization analysis of urban water use in the Yangtze River Delta area, China. Water Science and Technology, 62,300–310. | ||
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