اولویت‌بندی سناریوها در مدل مکان‌یابی مناطق مستعد تغذیه مصنوعی آبخوان جهت پخش سیلاب، مبتنی بر فرآیند تحلیل شبکه‌ای ANP (مطالعه موردی: آبخوان دشت خوی)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد مهندسی عمران - مهندسی و مدیریت منابع آب، گروه مهندسی عمران، واحد ارومیه، دانشگاه آزاد اسلامی، ارومیه، ایران.

2 استادیار گروه عمران، دانشکده فنی و مهندسی، دانشگاه ارومیه، ارومیه، ایران.

چکیده

با بهره‌گیری از روش تحلیل شبکه‌ای (ANP)، مدل تصمیم‌ساز جهت تسهیل اولویت‌بندی سناریوهای تغذیه مصنوعی-پخش سیلاب، تهیه گردید و به‌عنوان مطالعه موردی، نتایج مدل در دشت خوی (دارای محدودیت برداشت منابع آب زیرزمینی)، بررسی شدند. تعداد شش سناریو در مرحله اول تحقیق براساس 16 معیار فنی و روی‌هم‌گذاری لایه‌های GIS پیشنهاد گردیدند. در مرحله دوم، افزون بر معیارهای فوق، کلیه پارامترهای مؤثر در چهار خوشه فنی، اقتصادی، اجتماعی و زیست‌محیطی طبقه‌بندی شدند و پس از تعیین ارتباطات موجود در شبکه تصمیم‌سازی مطابق با روش دیمتل، از نرم‌افزار SuperDecisions، استفاده شد. براساس نتایج رتبه‌بندی ANP، سناریوی شماره سه واقع در شمال‌غربی منطقه مورد مطالعه با وزن نرمال 0.175 به‌عنوان برترین سناریو معرفی شد. این سناریو در مرحله اول تحقیق نیز که اولویت‌بندی در آن براساس روش AHP صورت گرفته بود، در رتبه اول قرار می‌گیرد ولی اولویت سایر سناریوها در دو روش باهم متفاوت است. تأثیرگذاری عوامل اقتصادی، اجتماعی و زیست‌محیطی و همچنین محدودیت ارتباطات داخلی در مدل AHP، عامل اصلی تفاوت در نتایج بوده است. تحلیل شبکه‌ای بدلیل در نظر گرفتن عواملی غیر از مسائل فنی، نسبت به روش AHP قابلیت بیشتری داشته و می‌توان مساﺋﻞ پیچیده همچون گزینش مناطق مستعد را با استفاده از آن با دقت بالایی تحلیل نمود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prioritizing Artificial Groundwater Nourishing-Flood Spreading Scenarios, Based on Analytical Network Process (ANP) (Case Study: Khoy Plain Aquifer)

نویسندگان [English]

  • Mehdi Shafiei 1
  • Mehdi Ghanbarzadeh Lak 2
1 M.Sc. Graduate of Civil Engineering - Engineering and Water Resources Management, Department of Civil Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
2 Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Urmia University, Urmia, Iran.
چکیده [English]

Using ANP methodology, a decision-support model conducted to facilitate the prioritization of artificial nourishing-flood spreading scenarios. As a case study the Khoy plain (with limited groundwater resources) was selected. In the first phase, six scenarios were proposed based on 16 technical criteria and overlaying of GIS shape files. In the second phase, in addition to above-mentioned criteria, other effective parameters were classified into four technical, economic, social, and environmental clusters. After determining the effective connections in the decision-making network based on DEMATEL technique, SuperDecisions software was used. Based on the results of ANP ranking, scenario #3 located in the northwest of the studied area, with a normal weight of 0.175 was selected as the best scenario. The result of the first phase of this study, in which scenarios were prioritized based on AHP method, was the same for the first rank, although the order of other scenarios faced changes. This can be due to the impact of economic, social and environmental factors, as well as the limitation of internal communication in the AHP model. Network analysis has more capability than the AHP method, so complex issues can be addressed, such as the selection of susceptible areas for artificial nourishing-flood spreading.

کلیدواژه‌ها [English]

  • Prioritizing
  • Flood spreading
  • Artificial Nourishing
  • analytical network process (ANP)
  • Site Selection
 
Abbasi F, Sohrab F, Abbasi N (2016) Evaluation of irrigation efficiencies in Iran. Journal of Irrigation and Drainage Structures Engineering Research 17(67):113-128 (In Persian)
Agide Z, Haileslassie A, Sally H, Erkossa T, Schmitter P, Langan S, Hoekstra D (2016) Analysis of water delivery performance of smallholder irrigation schemes in Ethiopia: Diversity and lessons across schemes, typologies and reaches. LIVES Working Paper 15, Nairobi, Kenya, 38P
Andres RJ, Gregg JS, Losey L, Marland G, Boden TA (2011) Monthly, global emissions of carbon dioxide from fossil fuel consumption. Journal of Chemical and Physical Meteorology 63(3):309-327
Forootan E, Rietbroek R, Kusche J, Sharifi M, Awange J, Schmid M, Famiglietti J (2014) Separation of large scale water storage patterns over Iran using GRACE altimetry and hydrological data. Journal of Remote Sensing of Environment 140:580-595
Fele F, Maestre J, Hashemy SM, de la Peña DM, Camacho EF (2014) Coalitional model predictive control of an irrigation canal. Journal of Process Control 24(4):314-325
Hosseinzade Z, Pagsuyoin S, Ponnambalam K, Monem MJ (2017) Decision making in irrigation networks: Selecting appropriate canal structures using multi-attribute decision analysis. Journal of Science of the Total Environment 601:177-185
Hashemy Shahdany SM, Hasani Y, Majidi Y, Maestre J (2016) Modern operation of main irrigation canals suffering from water scarcity based on an economic perspective. Journal of Irrigation and Drainage Engineering 143(3):136-147
Hashemi M, Hasani Y, Hormozi M (2017) Optimal water distribution within the main irrigation canal considering economic perspective in water shortages conditions. Journal of Iran-Water Resources Research 13(3):33-42 (In Persian)
Joodaki G, Wahr J, Swenson S (2014) Estimating the human contribution to groundwater depletion in the Middle East, from GRACE data, land surface models, and well observations. Journal of Water Resources Research 50(3):2679-2692
Karimi P, Qureshi AS, Bahramloo R, Molden D (2012) Reducing carbon emissions through improved irrigation and groundwater management: A case study from Iran. Journal of Agricultural water management 108:52-60
Madani  K (2014) Water management in Iran: what is causing the looming crisis?. Journal of Environmental Studies and Sciences 4(4):315-328
Molden DJ, Gates TK (1990) Performance measures for evaluation of irrigation water delivery systems. Journal of Irrigation and Drainage Engineering 116(6):804-823
Maciejowski JM (2002)  Predictive control: with constraints. Pearson Education Limited, Prentice Hall, London, 262P
Rogers DC, Goussard J (1998) Canal control algorithms currently in use. Journal of Irrigation and Drainage Engineering 124(1):11-15
Serra P, Salvati L, Queralt E, Pin C, Gonzalez O, Pons X (2016) Estimating water consumption and irrigation requirements in a long established mediterranean rural community by remote sensing and field data. Journal of Irrigation and Drainage 65(5):578-588
Schuurmans J (1997) Control of water levels in open channels. Ph.D. Dissertation, Delft University of Technology, The Netherlands
Shahverdi K, Monem MJ (2015) Application of reinforcement learning algorithm for automation of canal structures. Journal of Irrigation and Drainage 64(1):77-84
Shamsai A, Forghani A (2011) Conjunctive use of surface and ground water resources in arid regions. Journal of Iran-Water Resources Research 7(2):26-36 (In Persian)
van Overloop PJ, Schuurmans J, Brouwer R, Burt CM (2005) Multiple model optimization of proportional integral controllers on canals. Journal of Irrigation and Drainage Engineering 131(2):190-196
van Overloop PJ (2006) Model predictive control on open water systems. Delft University Press, 192P
van Overloop P, Miltenburg I, Clemmens A, Strand R (2008) Identification of pool characteristics of irrigation canals. In:Proc of National Conference on Advances in World Environmental and Water Resources Congress 2008, Ahupua A, 1-12
Wagemaker R (2005) Model predictive control on irrigation canals, application of various internal models. M.Sc. Thesis, Faculty of Information Technology and Systems, Delft University of Technology, Delft, Netherlands
Zamani S, Parvaresh Rizi A, Isapoor S (2015) The effect of design parameters of an irrigation canal on tuning of coefficients and performance of a PI controller. Journal of Irrigation and Drainage 64(4):519-534