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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 | ||
عنوان مقاله [English] | ||
Development of conjunctive surface and ground water use model with emphasis on the quality and quantity of water resources | ||
نویسندگان [English] | ||
Fatemeh Heydari1؛ Bahram Saghafian2؛ Majid Delavar3 | ||
چکیده [English] | ||
Many real water resources optimization problems involve conflicting objectives. In this study, multiobjective genetic algorithm NSGA-II, has been developed for optimization the conjunctive use of surface water and groundwater resources and optimal management of supply and demand of agricultural water. Here, optimal allocation of land and water resources to the dominant products in Najaf Abad plain, two surrogate models, Artificial Neural Network (ANN) and Genetic Programming (GP), has been linked with NSGA-II. Results according to Mean Squared Error and correlation coefficient values show the efficiency of alternative models for prediction the concentration of Total of Dissolved Solids (TDS) and groundwater level in observation wells. According to the final results of SO model, average drowdown in groundwater level is equal to 0.18 m in optimal conditions, compared to the current(pre-optimal) conditions has been reduced to one third,also average concentration of TDS decreased from 1258 mg/lit to 1229 mg/lit in optimal conditions. | ||
کلیدواژهها [English] | ||
Groundwater level, Multi-objective optimization, TDS concentration | ||
مراجع | ||
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