• صفحه اصلی
  • برنامه‌ریزی چندهدفه به منظور مدیریت اثرات کمی و کیفی بهره‌برداری بهینه از منابع آب زیرزمینی دشت شهریار

اشتراک گذاری

آدرس مقاله


کد مقاله : JEST-2110-5523 (R2) بازدید : 102 صفحه: 71 - 84

10.30495/jest.2022.63568.5523

نوع مقاله: پژوهشی

برنامه‌ریزی چندهدفه به منظور مدیریت اثرات کمی و کیفی بهره‌برداری بهینه از منابع آب زیرزمینی دشت شهریار

محورهای موضوعی : آب و محیط زیست

نیما صالحی شفا 1 (دانشجوی دکترای آبیاری و زهکشی، گروه علوم و مهندسی آب، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.)
حسین بابازاده 2 (استاد، گروه علوم و مهندسی آب، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران. (مسوول مکاتبات))
فیاض آقایاری 3 (استادیار، گروه زراعت و اصلاح نباتات، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران.)
علی صارمی 4 (استادیار، گروه علوم و مهندسی آب، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.)

تاریخ دریافت : 1400/07/10 تاریخ پذیرش : 1400/10/29 تاریخ انتشار : 1401/03/01

کلید واژه: منابع آب زیرزمینی, تغییرات سطح آب زیرزمینی, TDS, بهره‌برداری بهینه, پایداری سیستم آب زیرزمینی,

چکیده مقاله :

زمینه و هدف: با توسعه کشاورزی، صنعت و رشد جمعیت، بهره‌برداری از منابع آب زیرزمینی افزایش یافته و کمیت، کیفیت آن را نیز تحت تأثیر قرار داده است. مدیریت بهره‌برداری بهینه آب زیرزمینی برای جلوگیری از بروز مشکلات کمی و کیفی آبخوان‌ها ضروری می‌باشد. هدف از این تحقیق، بهره‌برداری بهینه از منابع آب زیرزمینی و بررسی اثرات کمی و کیفی آن بر آبخوان دشت شهریار است. روش بررسی: تغییرات سطح آب زیرزمینی و کیفیت آن از منظر شاخص TDS در آبخوان دشت شهریار در بازه زمانی سال آبی 93 تا 95، توسط شبکه عصبی مصنوعی شبیه‌سازی شده‌اند. سپس TDS آب زیرزمینی توسط رگرسیون برآورد شده است. و در نهایت از الگوریتم ژنتیک چند هدفه (NSGA-II) به منظور بهره‌برداری بهینه از منابع آب زیرزمینی و با هدف حداقل نمودن تغییرات سطح آب زیرزمینی و کل مواد جامد محلول آب زیرزمینی، استفاده شد. یافته‌ها: نتایج نشان داد، معیار ارزیابی RMSE در سه وضعیت آموزش، آزمایش و صحت سنجی برای تغییرات سطح آب زیرزمینی به ترتیب برابر 06e-27/1، 0025/0 و 003/0 و برای کل مواد جامد محلول آب زیرزمینی برابر 24/0، 64/27 و 608/14 می‌باشد و معیار ضریب همبستگی (R) در سطح 05/0 در سه وضعیت برای هر دو متغییر معنی دار بود. همچنین در بازه زمانی مورد مطالعه، حجم برداشت بهینه از آبخوان به میزان 12/29 درصد کاهش یافته و مقدار بهینه TDS آب زیرزمینی به طور میانگین، به اندازه 87/120 میلی گرم بر لیتر کمتر از برآورد شبکه عصبی مصنوعی است. و سطح آب زیرزمینی نیز به طور میانگین به اندازه 27/9 متر در سال افزایش یافته است. بحث و نتیجه‌گیری: نتایج حاصل نتایج نشان داد که روش شبیه سازی-بهینه سازی پیشنهادی به عنوان یک ابزار کاربردی با عملکرد مناسب و کم هزینه و با سرعت مطلوب می تواند با سیاست بهره‌برداری بهینه همزمان از چند عامل مؤثر پشتیبانی کند. همچنین مشکلات کمی و کیفی آبخوان را کاهش داده و باعث افزایش پایداری سیستم آب زیرزمینی می‌شود.

چکیده انگلیسی:

Background and Objective: Development of agriculture, industry and population growth, the exploitation of groundwater resources has increased and has affected its quality. In order to prevent the occurrence of quantitative and qualitative problems of aquifers, management of optimal operation of groundwater resources is essential. Therefore, the purpose of this study is the optimal utilization of groundwater resources and to investigate its quantitative and qualitative effects on the Shahriar plain aquifer. Material and Methodology: Groundwater level changes and its quality from the perspective of index (TDS) in Shahriar plain aquifer have been simulated by ANN. Then (TDS) of groundwater has estimated by regression. Finally, the multi-objective genetic algorithm (NSGA-II) was used for optimal utilization of groundwater resources and with the aim of minimizing groundwater level changes and total dissolved solids of groundwater. Findings: According to the results of the study, the evaluation criteria (RMSE) in three modes of training, testing and validation for groundwater level changes are equal to 1.27e-06, 0.0025 and 0.003, respectively, and for total dissolved solids of groundwater was calculated to be 0.24, 27.64 and 14.608 and the correlation coefficient (R) at the level (0.05) in three situations was significant for both variables. Also, during the study period, the volume of optimal withdrawal from the aquifer has decreased by 29.12 percent and the optimal amount (TDS) of groundwater on average, 120.87 Mg./l, has been calculated less than the estimate of artificial neural network. And Groundwater level has also increased by an average of 9.27 meters per year. Discussion and Conclusion: The results confirm that the proposed simulation-optimization method as an application tool with good performance, low cost and desirable speed can support several effective factors simultaneous with optimal operation policy. It also reduces the quantitative and qualitative problems of the aquifer and cause increases the stability of the groundwater system.  

منابع و مأخذ:


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    1. Rajaee, T., Ebrahimi, H. and Nourani, V., 2019. A review of the artificial intelligence methods in groundwater level modeling. Journal of hydrology, 572, pp.336-351.
      2.
    2. Banerjee, P., Singh, V.S., Chatttopadhyay, K., Chandra, P.C. and Singh, B., 2011. Artificial neural network model as a potential alternative for groundwater salinity forecasting. Journal of Hydrology, 398(3-4), pp.212-220.
      3.
    3. Coppola Jr, E.A., Rana, A.J., Poulton, M.M., Szidarovszky, F. and Uhl, V.W., 2005. A neural network model for predicting aquifer water level elevations. Groundwater, 43(2), pp.231-241.
      4.
    4. Moasheri, S.A., Rezapour, O.M., Beyranvand, Z. and Poornoori, Z., 2013. Estimating the spatial distribution ofgroundwater quality parameters of Kashan plain with integration method of Geostatistics-Artificial Neural Network Optimized by Genetic-Algorithm. International Journal of Agriculture and Crop Sciences, 5(20), p.2434.
      5.
    5. Safavi, H.R. and Enteshari, S., 2016. Conjunctive use of surface and ground water resources using the ant system optimization. Agricultural Water Management, 173, pp.23-34.
      6.
    6. Khatiri, K.N., Niksokhan, M.H., Sarang, A. and Kamali, A., 2020. Coupled Simulation-Optimization Model for the Management of Groundwater Resources by Considering Uncertainty and Conflict Resolution. Water Resources Management, 34(11), pp.3585-3608.
      7.
    7. Chakraei, I., Safavi, H.R., Dandy, G.C. and Golmohammadi, M.H., 2021. Integrated Simulation-Optimization Framework for Water Allocation Based on Sustainability of Surface Water and Groundwater Resources. Journal of Water Resources Planning and Management, 147(3), p.05021001.
      8.
    8. Elhamian, S.A.B., Rakhshandehroo, G. and Javid, A.H., 2021. Quantitative and Qualitative Optimization of Water Allocation in No Bandegan Aquifer using an Agent-based Approach. Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp.1-12.
      9.
    9. Ranjbar, A. and Mahjouri, N., 2020. Multi-objective freshwater management in coastal aquifers under uncertainty in hydraulic parameters. Natural Resources Research, 29(4), pp.2347-2368.
      10.
    10. Nouiri, I., Yitayew, M., Maßmann, J. and Tarhouni, J., 2015. Multi-objective optimization tool for integrated groundwater management. Water Resources Management, 29(14), pp.5353-5375.
      11.
    11. Kamali, A. and Niksokhan, M.H., 2017. Multi-objective optimization for sustainable groundwater management by developing of coupled quantity-quality simulation-optimization model. Journal of Hydroinformatics, 19(6), pp.973-992.
      12.
    12. Alizadeh, M.R., Nikoo, M.R. and Rakhshandehroo, G.R., 2017. Hydro-environmental management of groundwater resources: a fuzzy-based multi-objective compromise approach. Journal of Hydrology, 551, pp.540-554.
      13.
    13. Lal, A. and Datta, B., 2019. Multi-objective groundwater management strategy under uncertainties for sustainable control of saltwater intrusion: Solution for an island country in the South Pacific. Journal of environmental management, 234, pp.115-130.
      14.
    14. Yu, X., Sreekanth, J., Cui, T., Pickett, T. and Xin, P., 2021. Adaptative DNN emulator-enabled multi-objective optimization to manage aquifer− sea flux interactions in a regional coastal aquifer. Agricultural Water Management, 245, p.106571.
      15.
    15. McPhee, J. and Yeh, W.W.G., 2004. Multiobjective optimization for sustainable groundwater management in semiarid regions. Journal of water resources planning and management, 130(6), pp.490-497.
      16.
    16. Moridi, A., Tabatabaie, M.R.M. and Esmaeelzade, S., 2018. Holistic approach to sustainable groundwater management in semi-arid regions. International Journal of Environmental Research, 12(3), pp.347-355.
      17.
    17. Heydari, F., Saghafian, B. and Delavar, M., 2016. Coupled quantity-quality simulation-optimization model for conjunctive surface-groundwater use. Water Resources Management, 30(12), pp.4381-4397.
      18.
    18. Wang, Y., Yang, J. and Chang, J., 2019. Development of a coupled quantity-quality-environment water allocation model applying the optimization-simulation method. Journal of Cleaner Production, 213, pp.944-955.
      19.
    19. Kerebih, M.S. and Keshari, A.K., 2021. Distributed Simulation‐optimization Model for Conjunctive Use of Groundwater and Surface Water Under Environmental and Sustainability Restrictions. Water Resources Management, pp.1-19.
      20.
    20. Danapour, M., Fienen, M.N., Højberg, A.L., Jensen, K.H. and Stisen, S., 2021. Multi‐constrained catchment scale optimization of groundwater abstraction using linear programming. Groundwater.
      21.
    21. Sreekanth, J. and Datta, B., 2010. Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models. Journal of hydrology, 393(3-4), pp.245-256.
      22.
    22. Safavi, H.R. and Esmikhani, M., 2013. Conjunctive use of surface water and groundwater: application of support vector machines (SVMs) and genetic algorithms. Water Resources Management, 27(7), pp.2623-2644.
      23.
    23. Karamouz, M., Tabari, M.M.R. and Kerachian, R., 2007. Application of genetic algorithms and artificial neural networks in conjunctive use of surface and groundwater resources. Water International, 32(1), pp.163-176.
      24.
    24. Tabari, M.M.R. and Yazdi, A., 2014. Conjunctive use of surface and groundwater with inter-basin transfer approach: case study Piranshahr. Water resources management, 28(7), pp.1887-1906.
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      27.
    27. Heidarzadeh, N., 2017. A practical low-cost model for prediction of the groundwater quality using artificial neural networks. Journal of Water Supply: Research and Technology—AQUA, 66(2), pp.86-95.
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    28. Rezaei, F., Safavi, H.R., Mirchi, A. and Madani, K., 2017. f-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management. Journal of Hydro-environment Research, 14, pp.1-18.
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    30. Farhadi, S., Nikoo, M.R., Rakhshandehroo, G.R., Akhbari, M. and Alizadeh, M.R., 2016. An agent-based-nash modeling framework for sustainable groundwater management: A case study. Agricultural Water Management, 177, pp.348-358.
      31.
    31. Rezaei, F. and Safavi, H.R., 2020. f-MOPSO/Div: an improved extreme-point-based multi-objective PSO algorithm applied to a socio-economic-environmental conjunctive water use problem. Environmental Monitoring and Assessment, 192(12), pp.1-27.
      32.



     


     


     


     


 

 

 

 

 

 

 

 

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