تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,501 |
تعداد مشاهده مقاله | 124,093,920 |
تعداد دریافت فایل اصل مقاله | 97,198,722 |
توسعه مدل بهرهبرداری تلفیقی از منابع آب سطحی و زیرزمینی با تأکید بر کمیت و کیفیت منابع آب | ||
تحقیقات آب و خاک ایران | ||
مقاله 5، دوره 47، شماره 4، دی 1395، صفحه 687-699 اصل مقاله (1.02 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2016.59976 | ||
نویسندگان | ||
فاطمه حیدری1؛ بهرام ثقفیان2؛ مجید دلاور* 3 | ||
1دانشگاه تربیت مدرس | ||
2دانشگاه آزاد اسلامی، واحد علوم و تحقیقات | ||
3هیات علمی-دانشگاه تربیت مدرس | ||
چکیده | ||
بسیاری از مسائل واقعی تخصیص بهینه منابع آب شامل اهداف متضادی هستند. در این تحقیق، الگوریتم ژنتیک NSGA-II، بهمنظور بهینهسازی بهرهبرداری تلفیقی چندهدفه از منابع آب و مدیریت بهینه عرضه و تقاضای آب در بخش کشاورزی توسعه یافته است. بهمنظور تخصیص بهینه منابع آب و زمین به محصولات غالب در واحد هیدرولوژیکی نجفآباد، دو مدل جایگزین برنامهریزی ژنتیک و شبکه عصبی مصنوعی، با الگوریتم NSGA-II مرتبط شدهاند. نتایج مدل بر اساس پارامترهای آماری خطا، کارایی مدلهای جایگزین برای پیشبینی تراز آب زیرزمینی و غلظت کل جامدات محلول در تعدادی چاههای مشاهدهای نمونه را تأیید مینمایند. با توجه به نتایج نهائی الگوریتم شبیهسازی-بهینهسازی، مقدار متوسط افت تراز آب زیرزمینی در شرایط بهینه نسبت به شرایط موجود (65/0 متر) به 18/0 متر محدود شده است. بعلاوه، بر اساس الگوی بهینه، متوسط ماهیانه غلظت املاح در منطقه از 1258 به 1229 میلیگرم بر لیتر کاهش مییابد. | ||
کلیدواژهها | ||
بهینهسازی چندهدفه؛ تراز آب زیرزمینی؛ غلظت املاح | ||
مراجع | ||
Delavar, M. (2005). Assessment and modeling of Urmia lake level fluctuation and risk analysis of coastal areas. Master's thesis, Tarbiat Modares University, Tehran. Alimohammadi, S. And Hosseinzadeh, H. (2010). Optimization of conjunctive operation of surface and groundwater resources of Abhar river basin. Journal of Water and Wastewater, 20 (3), 75-87. Ghodrati, M. And Sabany, A. (2012). Mathematical models of groundwater. Tehran, Simaye Danesh Publication. Karamouz, M., Mohamreza Pourtabari, M. And Kerachian, R. (2004). Conjunctive use of surface and groundwater resources in southern of Tehran: Application of genetic algorithms and artificial neural network models. Annual conference of Iran Water Resources Management. Kanooni, A. (2013). Development of integrated model of optimal water allocation and distribution in irrigation networks. Ph. D. dissertation, Tarbiat Modares University, Tehran. Mohamreza Pourtabari, M., Maknoon, R. And Ebadi, T. (2009). Multi-objective optimization model for conjunctive use management using NSGA-II and SGAs algorithms. Journal of Water and Wastewater, 20 (1), 2-12. Ab and Tosee Paydar Consulting Engineering Co. (2010). Updating Studies on water resources and demand balance of Zayandehrood basin. Nagheli, S, Samani, N. and Pasandi, M. (2011). Assessment of balances and sustainable development of the Najaf Abad aquifer. 30th meeting of earth sciences. Bhattacharjya, R.K., and Datta, B. (2005). Optimal management of coastal aquifer using linked simulation optimization approach. Water Resources Management, 19(3), 295-320. Bhattacharjya, R.K., and Datta, B. (2009). ANN-GA-based model for multiple objective management of coastal aquifers. Journal of Water Resources Planning and Management-Asce, 135(5), 314–322. Cheng, F.Y., and Li, D. (1998). Genetic algorithm development for multiobjective optimization of structures. Am. Inst. Aeronaut. Astronaut. J, 36(6):1105–1112. Coe, J.J. (1990). Conjunctive use-advantages, constraints and examples. Journal of Irrigation and Drainage Engineering, 116(3), 427-443. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolutionary Computation, 6, 182-197. Dhar, A. and Datta, B. (2009). Multi-objective management of saltwater intrusion in coastal aquifers using linked simulation optimization-methdology development and performance evaluation. Journal of Hydrologic Engineering, ASCE, 14(12), 1263-1272. Doherty, J. (1994). PEST: a unique computer program for model-independent parameter optimisation. Water Down Under 94: Groundwater/Surface Hydrology Common Interest Papers; Preprints of Papers, 551. Essaid, H. I. (1990). A multilayered sharp interface model of coupled freshwater and saltwater flow in coastal systems: model development and application. American Geophysical Union. Gorelick, S.M. (1983). A review of distributed parameter groundwater management modelling methods. Water Resources Research, 19(2), 305-319. Horn, J., Nafpliotis, N., and Goldberg, D.E. (1994). A niched pareto genetic algorithm for multiobjective optimization. In: Proc. 1st IEEE Conf. Evolutionary Computation, IEEE World Congr. Computational Computation, Piscataway, 1, 82-87. Karamouz, M., Kerachian, R., and Zahraie, B. (2004). Monthly water resources and irrigation planning: case study of conjunctive use of surface and groundwater resources. Journal of Irrigation and Drainage Engineering, 130(5), 391-402. Karamouz, M., Rezapour Tabari, M., and Kerachian, R. (2007). Application of genetic algorithm and artificial neural networks in conjunctive use of surface and groundwater resources. Water International, 32(1), 163-176. Mahfoud, S.W. (1995). Population size and genetic drift in fitness sharing. In Whitley, D., Vose, M.D. (Eds.), Foundations of Genetic Algorithms 3. Morgan Kaufmann, San Francisco, 85–224. Makkeasorn, A., Chang, N.B., and Zhou, X. (2008). Short-term streamflow forecasting with global climate change implications – a comparative study between genetic programming and neural network models. Journal of Hydrology, 352(3–4), 336–354. McDonald, M.G., and Harbaugh, A.W. (1988). A modular three-dimensional finite-difference ground-water flow model: Techniques of Water-Resources Investigations of the United States Geological Survey. Book 6, Chapter A1, 586 p. Miller, S., and Labadie, J. (2003). A decision support system for optimal planning of conjunctive use progress. American Water Resources Association, 39(3), 517-528. Morel-Seytoux, H. J. (1975). A simple case of conjunctive surface-groundwater management. Journal of Groundwater, 13(6), 506-515. Morel-Seytoux, H. J., and Dally, C. J. (1975). A discrete kernel generator for stream aquifer studies. Water Resour. Res, 11(2), 253-260. Parasuraman, K., and Elshorbagy, A. (2008). Toward improving the reliability of hydrologic prediction: model structure uncertainty and its quantification using ensemble-based genetic programming framework. Water Resources Research, 44(12). Penn, R., Friedler, E. and Ostfeld, A. (2013). Multi-objective evolutionary optimization for greywater reuse in municipal sewer systems. Water Resources, 47(15), 5911-5920. Peralta, R. C., Contiller, R. A., and Terry, J. E. (1995). Optimal large-scale conjunctive water-use planning: Case study. J. Water Res. Plan. Manag, 121(6), 471-478. Peralta, R.C., and Kalwij, I. (2012). Groundwater Optimization Handbook: Flow, Contaminant Transport, and Conjunctive Management. International Water Association and CRC Press, Boca Raton, FL, USA, 539p. Peralta, R.C., Forghani, A., and Fayad, H. (2014). Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow. Journal of hydrology, 511, 776-785. Rao, S.V.N., Murthy, S.B., Thandaveswara, B.S., and Mishra, G.C. (2004). Conjunctive use of surface and groundwater for Coastal and Deltic systems. Journal of Water Resources Planning and Management, ASCE, 130(3), 255-267. Safavi, H.R., and Esmikhani, M. (2013). Conjunctive use of surface water and groundwater: Application of support vector machines (SVMs) and genetic algorithms. Water Res Manage, 27, 2623–2644. Safavi, H.R., Darzi, F., and Marino, M.A. (2010). Simulation–optimization modeling of conjunctive use of surface water and groundwater. Water Resources Management, 24(10), 1965-1988. Shiri, J., Sadraddini, A. A., Nazemi, A. H., Kisi, O., Landeras, G., Fakheri Fard, A., and Marti, P. (2014). Generalizability of Gene Expression Programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran. Journal of Hydrology, 508, 1-11. 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, 245-256. Srinivas, N., and Deb, K. (1995). Multiobjective optimization using nondominated sorting in genetic algorithms. J. Evol. Comput, 2(3), 221–248. Triana, E., Labadie, J., Gates, T., and Anderson, C. (2010). Neural network approach to stream-aquifer modeling for improved river basin management. Journal of Hydrology, 391, 235-247. Vamvakeridou-Lyroudia, L., Walters, G. and Savic, D. (2005). Fuzzy multiobjective optimization of water distribution networks. Journal of.Water Resources Planning and Management, 131(6), 467-476. Wagner, B. J. (1995). Recent advances in simulation–optimization groundwater management modelling. Rev Geophys, 33(2), 1021–1028. Wang, W.C., Chau, K.W., Cheng, C.T., and Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374(3–4), 294–306. Zechman, E., Mirghani, B., Mahinthakumar, G., and Ranjithan, S. (2005). A genetic programming-based surrogate model development and its application to a groundwater source identification problem. ASCE Conference Proceeding, 173, 341. Zheng, C. (1990). {MT3D}, A modular three-dimensional transport model. Zitzler, E., and Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evolutionary Computation, 3, 257-271. | ||
آمار تعداد مشاهده مقاله: 1,374 تعداد دریافت فایل اصل مقاله: 1,028 |