Evaluation of Crisis Resolution Strategies for Groundwater Revival Plan Using Fuzzy Best - Worst Multi Criteria Decision Model

Document Type : Research Paper

Authors

1 Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran.

2 Bargab Consulting Engineers Company, Gorgan, Iran.

Abstract

The importance of the proper implementation of the Groundwater Revival Plan (GRP) as one of the key approvals of the Supreme Water Council has been doubled due to the escalation of the groundwater crisis in most of Iran's aquifers. Therefore, the implementation of GRP principles is the only way to overcome the crisis and the related challenges. In this study, the management crisis of Iran's groundwater resources is re-evaluated from the perspective of the administrative, social, and legal system with the GRP challenging approach. Then, the best -worst fuzzy multi - criteria decision model in Lingo9 software is developed by collecting the opinions of 23 GRP experts, based on three general criteria of change in administrative structure (C1), policy structure (C2) and modification of GRP legal structure (C3), which includes 18 sub-criteria. The results showed that timely and appropriate allocation of financial resources (S22), creating incentive packages for farmers to improve the cultivation pattern (S23) and creating serious efforts at all levels of management in the field of implementation, continuity, and removal of obstacles (S21) respectively with weights. 0.2247, 0.1098, and 0.0946 - with more than 40% of the total importance - occupy the first to third ranks.

Keywords


  1. Srinivasan, V., Lambin, E. F., Gorelick, S. M., Thompson, B. H., & Rozelle, S. (2012). The nature and causes of the global water crisis: Syndromes from a meta-analysis of coupled human water studies. Water Resources Research, 48(10).
  2. Yeganeh, B. Shafie Pour Motlagh, M. Rashidi, Y. &Kamalan, H. (2012). Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model. Atmospheric Environment, 55: 357-365. doi.org/10.1016/j.atmosenv.2012.02.092
  3. Motiee, H., Salamat, A., & Bean, E. (2012). Drought as a water related disaster; a case study of Oroomieh Lake. Aqua-Lac, 4(2): 7-18.
  4. Mohammadi, H. & Shamsipoor, A. (2003). The effect of recent droughts on the decline of groundwater resources in the plains north of Hamadan, Journal of Geographical Research, No. 45, Summer, pp. 130-115
  5. Michel, D., Pandya, A., Hasnain, S. I., Sticklor, R., & Panuganti, S. (2012). Water challenges and cooperative response in the Middle East and North Africa. Brookings Institution, Washington, DC.
  6. Ashrafi, S. M. (2019). Investigating Pareto Front Extreme Policies Using Semi-distributed Simulation Model for Great Karun River Basin. Journal of Hydraulic Structures, 5(1), 75-88.
  7. World Bank. (2007).Making the most of scarcity accountability for better water management results in the Middle East and North Africa. MENA development report: Washington DC.
  8. Zolpirani, N. M., Amirnejad, H., & Shahnazari, A. (2015). Calculating the economic value of water in paddy farms in the area of Alborz dam. Journal of Novel Applied Science, 4(2): 197-201.
  9. Seckler, D., Amarasinghe, U., Molden, D., de Silva, R., & Barker, R. (1998). World Water Demand and Supply, 1990 to 2025: Scenarios and Issues. International Water Management Institute. Colombo, Sri Lanka.
  10. Goodarzi, Z. Chizari, M., Bagheri, A. & Sedighi, H., (2016). The need to use smart meters in agricultural wells, the Second National Conference on Mechanization and New Technologies in Agriculture, Ramin Khuzestan University of Agriculture and Natural Resources, Ahvaz, Iran.
  11. Farzaneh, M., (2016). Institutional Recommendations Related to the Implementation of the Ground Water Revival Plan in the Rafsanjan Study Area, 2nd National Iranian Irrigation and Drainage Congress, Isfahan University of Technology, Isfahan, Iran
  12. Donyaii, A.R., Sarraf, A.P., &Ahmadi, H. (2020). Multi-Objective Optimal Utilization Policy of Boostan Dam Reservoir Using Whale and NSGA-II Algorithms Based on Game Theory and Shannon Entropy Method, Iranian water researches Journal, In Press. [in Persian].
  13. Afkhamifar S. &Sarraf, A. (2020). Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization. Watershed Engineering and Management, 12(2): 351-364. doi: 10.22092/ijwmse.2019.126515.1669, [in Persian].
  14. Roudi-Fahimi, F., Creel, L., & De Souza, R. M. (2002). Finding the balance: Population and water scarcity in the Middle East and North Africa (pp. 1-8). Washington, DC: Population Reference Bureau.
  15. Khodarahimi, S., & Deghani, H. (2012). Hopefulness, positive and negative emotions in rural residents with drink water shortage: an Iranian case study. Problems of Psychology in the 21st Century, 3: 32-41.
  16. Fani, A., Ghazi, I. & Malekian, A. (2016). Challenges of Water Resource Management in Iran. American Journal of Environmental Engineering. 6(4): 123-128.
  17. Ashrafi, S. M., Ashrafi, S. F., &Moazami, S. (2017). Developing self-adaptive melody search algorithm for optimal operation of multi-reservoir systems. Journal of Hydraulic Structures, 3(1), 35-48.
  18. Lehane S. (2014). The Iranian Water Crisis. Strategic Analysis Paper. Future Directions International.
  19. Madani, K. (2014). Water management in Iran: what is causing the looming crisis?. Journal of environmental studies and sciences, 4(4): 315-328.
  20. Donyaii, A.R., Sarraf, A. and Ahmadi, H. (2020). Using composite ranking to select the most appropriate Multi-Criteria Decision Making (MCDM) method in the optimal operation of the Dam reservoir, Journal of Hydraulic Structures, 6(2):1-22
  21. Ashrafi, S. M., & Mahmoudi, M. (2019). Developing a semi-distributed decision support system for great Karun water resources system. Journal of Applied Research in Water and Wastewater, 6(1), 16-24.
  22. Zamanirad, M., Sarraf, A., Sedghi, H. Saremi, A. & Rezaee, P. (2020). Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms. Nat Resour Res 29, 1127–1141 https://doi.org/10.1007/s11053-019-09490-9
  23. Ferrett R. L., Ward Robert M. (1983). Agricultural land use planning and ground water quality, Growth and Change Journal, Lexington: Vol. 14, Iss.
  24. Bhat A. and Blomquist W. (2004). Policy, politics and water management in the Guadalquirir River Basun; Spain, Water Resource Research, Vol. 40.
  25. Yang D., Li C., Hu Lei Z., Yang S., Kusuda T., Koik T. and Musiake K. (2004).Analysis of water resources variability in the Yellow River of china during the last half century using historical data; Water Resource Research, Vol. 40.
  26. farajzadeh asl m., hosseini a.b. (2007). Neishabour plain water crisis analysis, Journal of the Teacher of Humanities (Special Issue of Geography), 11(53): 215-238
  27. Mohammadjani I. & Yazdanian N.(2014). The analysis of water crisis conjecture in Iran and the exigent measures for its management, Ravand (Economic Research Trend), 21: 117-144.
  28. Alipour, A., Hassani, Kh. & Legzian, R., (2016). Review of the Ground Water Revival Plan (Case study: Neyshabur Crisis Forbidden Pilot Plain), Second National Irrigation and Drainage Congress of Iran, Isfahan University of Technology, Isfahan,
  29. Rahmani, H., (2016). Necessities of implementing the Ground Water Revival Plan, 6th National Conference on Water Resources Management, Iran University, Kurdistan, Kurdistan,
  30. Donyaii,A.R., Sarraf, A. and Ahmadi, H. (2020). Application of a New Approach in Optimizing the Operation of the Multi-Objective Reservoir, Journal of Hydraulic Structures, 6(3):1-22. Doi: 10.22055/JHS.2020.34556.1145
  31. Donyaii, A.R., Sarraf, A.P., & Ahmadi, H. (2020). Optimization of Reservoir Dam Operation Using Gray Wolf, Crow Search and Whale Algorithms Based on the Solution of the Nonlinear Programming Model Journal of Water and Soil Science, In Press. [in Persian].
  32. Mianabadi, H. & Afshar, A., (2010). A heterogeneous fuzzy group decision in integrated water resources management, Sharif Civil Engineering Journal, Volume 2-27, No. 4, pp. 123-131 (Technical Note).
  33. Jahromi, H.N. Hamedani, M.J. Dolatabadi, S.F. & Abbasi, P. (2014). Smart energy and water meter: a novel vision to groundwater monitoring and management, 12th International Conference on Computing and Control for the Water Industry, CCWI2013, Procedia Engineering 70:877 – 881
  34. Layeghi Moghadam, p., Rasouli M. & Soleimanpour, M., (2015).Types of Modern Irrigation Methods in Agriculture, Second Scientific Research Congress on Development and Promotion of Agricultural Sciences, Natural Resources and Environment of Iran, Tehran, Association for Development and Promotion of Basic Sciences and Techniques.
  35. Zadeh , L.A. (1965). Fuzzy sets, Inf. Control 8 (3): 338–353.
  36. Carlsson, C., & Fullér, R. (2001). On possibilistic mean value and variance of fuzzy numbers. Fuzzy sets and systems, 122(2): 315-326.
  37. Zhao, H., & Guo, S. (2014). Selecting green supplier of thermal power equipment by using a hybrid MCDM method for sustainability. Sustainability, 6(1), 217-235.
  38. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega.53, 49-57.
  39. Lootsma, F.A. (1980). Saaty’s priority theory and the nomination of a senior professor in operations research, Eur. J. Oper. Res. 4 (6): 380–388.
  40. Guo, Sen & Zhao, Haoran. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems. 121. 10.1016/j.knosys.2017.01.010.