• صفحه اصلی
  • مقایسه مدل‌های TOPSIS، SAW، ELECTRE و VIKOR به منظور بررسی اولویت فرسایش‌پذیری و سیل‌خیزی زیرحوزه‌های آبخیز

اشتراک گذاری

آدرس مقاله


کد مقاله : JEST-1906-4731 (R4) بازدید : 140 صفحه: 79 - 99

10.30495/jest.2022.45404.4731

نوع مقاله: پژوهشی

مقایسه مدل‌های TOPSIS، SAW، ELECTRE و VIKOR به منظور بررسی اولویت فرسایش‌پذیری و سیل‌خیزی زیرحوزه‌های آبخیز

محورهای موضوعی : محیط زیست حوزه های آبخیز رودخانه ها

مهدی تیموری 1 , احسان الوندی 2

1 - استادیار مجتمع آموزش عالی شیروان.
2 - فارغ‌التحصیل دکتری علوم و مهندسی آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان.  (مسوول مکاتبات)

تاریخ دریافت : 1398/03/19 تاریخ پذیرش : 1398/10/23 تاریخ انتشار : 1401/02/01

کلید واژه: روش‌های تصمیم گیری چند معیاره, حوضه رودخانه حبله رود, سیل‌خیزی, فرسایش پذیری,

چکیده مقاله :

زمینه و هدف: تعیین مناطق مولد سیل و اولویت بندی زیرحوضه ها از نظر قابلیت سیل خیزی و رسوب زایی می تواند در مدیریت بهتر حوزه آبخیز نقش مهمی داشته باشد. بدین منظور هدف این تحقیق اولویت بندی قابلیت سیل خیزی و رسوب زایی زیرحوزه های آبخیز بنکوه با استفاده از روش های تصمیم گیری چند معیاره TOPSIS، SAW، ELECTRE و VIKOR بود. مواد و روش ها: در مطالعه حاضر شاخص های ارزیابی مساحت، ارتفاع متوسط حوضه، تراکم زهکشی، شماره منحنی، درصد پوشش، تولید رسوب، ضریب گراولیوس، نسبت تحویل رسوب، شیب متوسط حوضه، ارتفاع رواناب و زمان تمرکز استفاده شد. سپس با استفاده از روش AHP وزن هریک از شاخص ها برآورده شده است. در ادامه پس از تشکیل ماتریس تصمیم با 18 گزینه (زیرحوضه) و 11 معیار(شاخص ارزیابی) به منظور اولویت بندی زیر حوضه ها از فنون TOPSIS، SAW، ELECTRE و VIKOR استفاده شد. به منظور تلفیق رتبه فنون پیشنهادی از روش های بردا و کپ لند استفاده شد. از روش مجموع مربعات خطا (RSS)، برای تعیین نزدیک ترین روش به نتیجه نهایی استفاده شد. همچنین به منظور اعتبارسنجی مدل ها به برآورد درصد تغییرات و شدت تغییرات پرداخته شد. یافته ها: نتایج نشان داد، شاخص ارتفاع رواناب بیشترین وزن (18/0) و شاخص ضریب گراولیوس کمترین وزن (028/0) را از نظر کارشناسان به خود اختصاص داده است. نتایج اولویت بندی نشان داد، در روش های SAW، TOPSIS و VIKOR زیرحوضه های 9، 4 و 2 اولویت های اول تا سوم را به خود اختصاص داده اند و در حالت بحرانی تری قرار دارند، اما در روش ELECTRE زیر حوضه های 9، 4، 2 و 7 در حالت بحرانی تری قرار گرفته اند. با توجه به نتایج حاصل از تلفیق رتبه فنون پیشنهادی، زیرحوضه های 9، 4 و 2 به ترتیب در اولویت های اول تا سوم قرار دارند و از وضعیت بحرانی تری نسبت به بقیه زیرحوضه ها برخوردار هسستند. مطالعات میدانی نتایج تحقیق را به خوبی نشان می دهد، چرا که زیر حوضه 9، 4 و 2 دارای بیشترین حد فرسایش، شیب زیاد و بارندگی شدید هستند. همچنین پهنه های دارای قابلیت سیل خیزی و رسوب زایی در منطقه نشان داد که 32% منطقه در خطر زیاد و خیلی زیاد قرار دارند. نتایج مجموع مربعات خطا نشان داد، روش VIKOR کمترین خطا و روش ELECTRE بیشترین خطا را نسبت به رتبه بندی نهایی دارا است. همچنین روش های TOPSISوVIKOR با کمترین درصد تغییرات (72/59 درصد) در رتبه اول و روش ELECTRE با بیشترین درصد تغییرات (27/65 درصد) در رتبه آخر قرار دارند. کمترین شدت تغییرات نیز در روش VIKOR (59/4) و بیشترین مقدار شدت تغییرات در روش ELECTRE (61/5) مشاهده شد. بحث و نتیجه‌گیری: فنون تصمیم گیری چند معیاره، یک رویکرد عملی و مناسب برای تصمیم گیری بهتر بر مبنای علوم ریاضیات و بهینه سازی هستند. بنابراین می توان گفت به منظور مدیریت زیر حوضه ها از نظر اجرای عملیات زیستی و سازه ای کنترل سیل با چند تابع هدف و با توجه به عملیات پرهزینه آبخیزداری، می توان از روش های تصمیم گیری چند معیاره استفاده کرد تا اولویت بندی بر اساس یک منطق ریاضی انجام گیرد.

چکیده انگلیسی:

Background and Objective: To determine and zonation of flood prone hazardous area and prioritization the sub-basins in terms of flood potential can have great contributions in promising watershed management. The present research aims to prioritize flood and sedimentation potential in the sub-basins of the Bonekooh watershed using TOPSIS, SAW, ELECTRE and VIKOR methods. Material and Methodology: In this present study, we used area estimation indices, drainage density, gravel coefficient, basin average height, basin average slope, curve number, sediment yield, cover percentage, sediment delivery ratio, runoff height and concentration time as an important indicators affecting water permeability, runoff production and, consequently, the potential for flooding and sedimentation. Then, using the Analytic Hierarchy Process (AHP) method, the weight of each index is met. Following the formation of decision matrix with 18 options (sub-basins) and 11 criteria (evaluation index), Technique for order Preference by Similarity to ideal Solution (TOPSIS), Simple Additive Weighting (SAW), Elimination Et Choice Translation Reality (ELECTRE) and Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) techniques were used to prioritize sub-basins. Borda and Copland methods were used to combine the rank of proposed techniques. The residual sum of squares (RSS) method was used to determine the closest method to the final result. Also, in order to validate the models, we estimated the percentage change and the intensity of the changes. Findings: The results showed that the highest runoff height index (0.18) and the gravel coefficient had the lowest weight (0.028), according to experts. Prioritization results showed that in SAW, TOPSIS and VIKOR methods, sub-zones 9, 4, and 2 ranked first to third priorities respectively and are in a more critical situation, but in the ELECTRE approach sub-basins 9, 4, 2 and 7, respectively, have the first to third priorities. Considering the results of the combined ranking of the proposed techniques, sub-basins 9, 4 and 2 are in first to third priority, respectively, and have a more critical situation than the rest of the sub-basins. Also, zones with flood and sedimentation potential in the area showed that 32% of the area in high and very high risk. The results of the sum squared error showed that the VIKOR method had the least error and the ELECTRE method had the most error than the final ranking. Also, the TOPSIS and VIKOR methods with the lowest percentage of change (59.72%) were ranked first and the ELECTRE method with the highest percentage change (65.27%) is the last rank. The lowest intensity was also observed in the VIKOR method (4.59) and the highest intensity variation was observed in ELECTRE method (5.61). Discussion and Conclusion: Therefore, it can be said that multi-criteria decision-making techniques are a practical and appropriate approach for better decision-making based on mathematical sciences and optimization. Therefore, these types of low-cost and fast-track research can be prioritized to protect watersheds.

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_||_

  1. Rao, L.A.K. Rehman, A.Z. and Alia, Y. 2011. Morphometric analysis of drainage basin using remote sensing and GIS techniques: a case study of Etmadpur Tehsil, Agra District, U.P. International Journal of Research in Chemistry and Environment. 1(2): 36-45.
    2.
  2. Nkwunonwo, U.C., Whitworth, M., and Baily, B. 2015. A Review and critical analysis of the efforts towards urban flood reduction in the lagos region of Nigeria, Nat., Hazards Earth Syst., Sci. Discuss. PP. 3897-3923.
    3.
  3. Wang, G. Gertner, G., Fang, S., and Anderson, A.B. 2003. Mapping multiple variables for predicting soil loss by geostatistical methods with TM images and a slope map. Photogrammetric Engineering and Remote Sensing. 69(8): 889- 898.
    4.
  4. Sarangi, A., C.A., Madramootoo, and C., Cox. 2004. A decision support system for soil and water conservation measures on agricultural watersheds. Land Degradation and Development. Land Degradation and Development, 15 (49:( 49-63
    5.
  5. Jang T, Vellidis G, Hyman JB, Brooks E, Kurkalova LA, Boll J, Cho     2013. Model   for Prioritizing      best management practice implementation: sediment    load    reduction. Environ. Manage.51. 209–224.
    6.
  6. Aher, P.D. Adinarayana, J. and Gorantivar, S.D. 2013. Prioritization of Watersheds using multi-criteria evaluation through fuzzy analytical hierarchy process. Agric Eng Int: CIGR Journal. 15(1): 11-18.
    7.
  7. Zavadskas, EK., Turskis, Z., Kildienė, S., 2014. State of art surveys of overviews on MCDM/MADM. Technological and Economic Development of Economy. 20:165- 179.
    8.
  8. Parhizkar, A., A. Ghafarie gilande. 2006. GIS analysis and multi-criteria decision. Samt Publications. 597 pp.
    9.
  9. Vivien, Y.C., P.L., Hui, H.L., Chui, J.H.L., James, H.T., Gwo, and S.Y., Lung. 2011. Fuzzy MCDM approach for selecting the best environment-watershed plan. Journal of Applied Soft Computing 11, 265–275.
    10.
  10. Kaya, , C., Kahraman. 2011. Fuzzy multiple criteria forestry decision making based on an integratedVIKOR and AHP approach. Journal of Expert Systems with Applications 38, 7326–7333.
    11.
  11. Ahani Amineha, Z., Al-DinHashemian,J., Magholi, A. 2017. Integrating Spatial Multi Criteria Decision Making (SMCDM) with Geographic Information Systems (GIS) for delineation of the most suitable areas for aquifer storage and recovery (ASR). Journal of Hydrology, 551: 577-595.
    12.
  12. Gogate, N., Kalbar, P., Raval, P. 2017. Assessment of stormwater management options in urban contexts using Multiple Attribute Decision-Making. Journal of Cleaner Production. 142(4): 2046-2059.
    13.
  13. Nitheshnirmal, S., Bhardwaj, A., Dineshkumar, C., and Abdul Rahaman, S. 2019. Prioritization of Erosion Prone Micro-Watersheds Using Morphometric Analysis coupled with Multi-Criteria Decision Making. Proceedings. 24(11):1-6.
    14.
  14. Aouragh, H.M., Essahlaoui, A. 2018. A TOPSIS approach-based morphometric analysis for sub-watersheds prioritization of high Oum Er-Rbia basin, Morocco. Spat. Inf. Res. 26: 187–202.
    15.
  15. Balasubramani, K., Gomathi, M., Bhaskaran, G., Kumaraswamy, K. 2019. GIS-based spatial multi-criteria approach for characterization and prioritization of micro-watersheds: A case study of semi-arid watershed, South India. Appl. Geomat. 1–19.
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  16. Jain,, Ramsankaran, R. 2019. GIS-based integrated multi-criteria modelling framework for watershed prioritisation in India—A demonstration in Marol watershed. Journal of Hydrology. 578, 124131.
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  17. Javed A, Khanday MY, Ahmed R. Prioritization of watersheds based on morphometric and landuse analysis using RS and GIS techniques. Journal of the Indian society of Remote Sensing. 2009; 37. 261-274.
    18.
  18. Aher P,   Adinarayana   J, Gorantiwar   2014. Quantification  of  morphometric characterization      and prioritization for management  planning  in  semi-arid  tropics  of India:  A  remote  sensing  and  GIS  approach. Journal of Hydrology. 511. 850-860.
    19.
  19. Chandrashekara, H., Lokeshb, K., Sameenac, M., roopad, J., and rangannae, G. 2015. GIS –Based Morphometric Analysis of Two Reservoir Catchments of Arkavati River, Ramanagaram District,    Aquatic Procedia, INTERNATIONAL CONFERENCE ON WATER RESOURCES,  COASTAL AND OCEAN ENGINEERING (ICWRCOE  2015), vol 4, pp. 1345 – 1353.
    20.
  20. Arami, H., Alvandi, E., Forootan, M., Tahmasebipour, N., Karimi Sangchini, E. 2017. Prioritization of   watersheds   in   order   to   perform   administrative measures using fuzzy analytic hierarchy process, Journal of the Faculty of Forestry Istanbul University, 67(1): 13-21.
    21.
  21. ArabAmeri,, Pourghasemi, H., Cerda, A. 2018. Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. Science of The Total Environment, 613–614, 1385-1400.
    22.
  22. Zavadskas, EK., Turskis, Z., 2011. Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and Economic Development of Economy. 17:397-427.
    23.
  23. Ghaleno, M.R.D., Meshram, S.G., Alvandi, E. 2020. Pragmatic approach for prioritization of flood and sedimentation hazard potential of watersheds. Soft Computing. https://doi.org/10.1007/s00500-020-04899-4.
    24.
  24. Meshram, S.G., Alvandi, E., Singh, V. P., Meshram, Ch. 2019. Comparison of AHP and fuzzy AHP models for prioritization of watersheds. Soft Computing. https://doi.org/10.1007/s00500-019-03900-z.
    25.
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