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Manuscript ID : JEST-1906-4731 (R4) Visit : 169 Page: 79 - 99

10.30495/jest.2022.45404.4731

Article Type: Original Research

Comparison of Models TOPSIS, SAW, ELECTRE and VIKOR in order to the prioritization of sedimentation and flood hazard of watersheds

Subject Areas : River Basin Environment

Mehdi Teimouri 1 , Ehsan Alvandi 2 *

1 - Assistant Professor Higher Education Complex of Shirvan, Khorsasn Shomali, Shirvan, Iran.
2 - Ph.D. Graduate in Watershed Management Sciences and Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Golestan, Gorgan, Iran. *(Corresponding Author)

Received: 2019-06-09 Accepted : 2020-01-13 Published : 2022-04-21

Keywords: Hablehroud River Basin, Flooding, sedimentation, Multi-criteria decision making,

Abstract :

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.

References:


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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.
    16.
  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.
    17.
  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.
  25. Keshtkar, A.R., Salajegheh, A., Sadoddin, A., and Allan, M.G. 2013. Application of Bayesian networks for sustainability assessment in catchment modeling and management, case study: The Hablehrood river catchment. Ecological Modeling. 268: 48–54.
    26.
  26. Nitheshnirmal, S., Thilagaraj, P., Rahaman, S.A., Jegankumar, R. 2019. Erosion risk assessment through morphometric indices for prioritisation of Arjuna watershed using ALOS-PALSAR DEM. Model. Earth Syst. Environ. 1–18.
    27.
  27. Amiri, M., Pourghasemi, H.R., Arabameri, A., Vazirzadeh, A., Yousefi, H., Kafaei, S. 2019. Prioritization of Flood Inundation of Maharloo Watershed in Iran Using Morphometric Parameters Analysis and TOPSIS MCDM Model. Spatial Model. GIS R Earth Environ. Sci. 371–390.
    28.
  28. Mahdavi, M., 1999. Applied Hydrology, Tehran University Publishers, 401 pp.
    29.
  29. Ghaemi, H., 1994. Studies to supplementary identification of watershed project in Karkheh watershed. Watershed management department of the Ministry of Jihad, pp 195. (In Persian).
    30.
  30. Amani, F., 1998. Feasibility of flooding. Master Thesis, Shahid Beheshti University, pp 182. (In Persian)
    31.
  31. Salajegheh, A., S. Razavizadeh, & S.  Salajegheh, 2010. Priority of flooding in Tangab sub basins in Fars province, using Scalogram model. The first international conference on plant, water, soil 7 weather modeling, Kerman, Iran. (In Persian)
    32.
  32. Mtibaa, S., Hotta, N., and Irie, M. 2018. Analysis of the efficacy and cost-effectiveness of best management practices for controlling sediment yield: A case study of the Joumine watershed, Tunisia, Science of the Total Environment. 617:1–16.
    33.
  33. Chen, F.H., Tzeng, G.H., Chang, C.C., 2015. Evaluating the enhancement of corporate social responsibility websites quality based on a new hybrid MADM model. Int. J. Inf. Decis. Mak. 14, 697–724.
    34.
  34. Sadoddin, A., Shahabi, M., and Bai, M. 2017. Integrated watershed assessment and management Principles and approaches for modeling and decision making. Gorgan University of Agricultural Sciences and Natural Resources Publishing. 170 p. (In Persian)
    35.
  35. Georgiou, D., Mohammed, E.S., Rozakis, S., 2015. Multi-criteria decision making on the energy supply configuration of autonomous desalination units. Renew. Energy 75, 459–467.
    36.
  36. El-Santawy, MF. 2012. A VIKOR Method for Solving Personnel Training Selection Problem. International Journal of Computing Science. 1 (2): 9-12.
    37.
  37. Badri, S.A., 2003. Models of rural planning. Pamphlets Practical Lesson in Geography and Rural Planning. Payame Noor University, p. 126.
    38.
  38. Ahmad Rather, M., Satish Kumar, J., Farooq, M., Rashid, H. 2017. Assessing the influence of watershed characteristics on soil erosion susceptibility of Jhelum basin in Kashmir Arab. J. Geosci. 10, 59.
    39.
  39. Biswas, H., Raizada, A., Mandal, D., Kumar, S., Srinivas, S., Mishra, P.K. 2015. Identification of areas vulnerable to soil erosion risk in India using GIS methods. Solid Earth. 6, 1247–1257.
    40.
  40. Altaf, S., Meraj, G., Ahmad Romshoo, S. 2014. Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan Environ. Monit. Assess. 186, 8391–8412.
    41.
  41. Badar, B., Romshoo, S.A., Khan, M.A. 2013. Integrating biophysical and socioeconomic information for prioritizing watersheds in a Kashmir Himalayan lake: a remote sensing and GIS approach. Environ. Monit. Assess. 185, 6419–6445.
    42.
  42. Romshoo, S.A., Rashid, I. 2012. Assessing the impacts of changing land cover and climate on Hokersar wetland in Indian Himalayas. Arab. J. Geosci. 7, 143–160.
    43.
  43. Souissi, D., Zouhri, L., Hammami, S., Msaddek, M., Zghibi, A., and Dlala, M. 2019. GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto International.
    44.
  44. Ghanavati, E., Ahmadabadi, A., Sadeghi, M. 2019. The Selection of Susceptible Reservoirs to Flood Storage with an Emphasis on Landform Characteristics and Spatial Multi-Criteria Decision-Making Methods in Qomroud Watershed. Hydrogeomorphology. 5(18):139-159.
    45.

 

 

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