تحلیل جبهه پارتو در تخصیص بهینه منابع آب با استفاده از الگوریتم چندهدفه اسب (MOHO)

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران.

10.22059/jwim.2026.401208.1254

چکیده

مدیریت منابع آب در مناطق نیمه‌خشک با چالش‌هایی همچون نوسانات اقلیمی، تضاد عرضه و تقاضا و محدودیت‌های محیط‌زیستی همراه است. در این پژوهش، الگوریتم چندهدفه اسب (MOHO) برای نخستین بار جهت بهره‌برداری بهینه از مخزن قرنقو توسعه داده شد. دو هدف اصلی شامل کاهش آسیب‌پذیری ناشی از کمبود آب و افزایش اطمینان‌پذیری تأمین تقاضا مورد بررسی قرار گرفت. مقایسه عملکرد MOHO با مدل مرجع MOGA و تابع آزمون فونسکا–فلمینگ نشان داد که الگوریتم جدید توانایی مناسبی در دست‌یابی به جبهه پارتو و حفظ تنوع پاسخ دارد. تحلیل نتایج دوره پایه (2000–1971) نشان داد که مقدار آسیب‌پذیری در طیف راه‌حل‌ها بین حدود 15 تا 36 درصد و اطمینان‌پذیری بین حدود 29 تا 70درصد تغییر می‌کند. نقطه سازشی جبهه پارتو در محدوده آسیب‌پذیری 31 تا 33 درصد و اطمینان‌پذیری 40 تا 55 درصد قرار گرفت. هم‌چنین بررسی رفتار مخزن بیان‌گر وجود عدم‌تطابق زمانی میان ورود طبیعی و تقاضای انسانی بوده و ضرورت برنامه‌ریزی دقیق رهاسازی و مدیریت ذخیره را نشان می‌دهد. در نهایت، مجموعه‌ای از اقدامات مدیریتی برای کاهش کسری، کنترل سرریز و افزایش تاب‌آوری سامانه پیشنهاد شد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Pareto Front Analysis in Multi-Objective Optimal Water Resources Allocation Using the MOHO Algorithm

نویسندگان [English]

  • Leila Shakarami
  • Parisa-Sadat Ashofteh
Department of Civil Engineering, University of Qom, Qom, Iran.
چکیده [English]

Water resources management in semi-arid regions is challenged by climate variability, supply–demand imbalances, and environmental constraints. In this study, the Multi-Objective Horse Optimization (MOHO) algorithm was developed to optimize the operation of the Garanqo Reservoir in northwestern Iran. Two main objectives were considered: (1) minimizing vulnerability caused by water shortages and (2) maximizing reliability in meeting downstream water demands. The performance of MOHO was compared with the reference Multi-Objective Genetic Algorithm (MOGA) using the Fonseca–Fleming test function. Results showed that MOHO achieved a well-distributed Pareto front and maintained solution diversity effectively. Analysis of the base period (1971–2000) revealed that vulnerability ranged from 15% to 36%, while reliability ranged from 29% to 70% across the set of Pareto-optimal solutions. The compromise zone was identified within vulnerability levels of 31–33% and reliability levels of 40–55%, highlighting the trade-off between the two conflicting objectives. Reservoir performance analysis also showed a significant temporal mismatch between natural inflows and human water demand, emphasizing the need for accurate release planning and storage management. Finally, several management strategies were proposed to reduce shortages, prevent spill losses, and enhance system resilience under variable hydrological conditions.

کلیدواژه‌ها [English]

  • MOGA model
  • MOHO algorithm
  • Multi-objective optimization
  • Water resource allocation

مراجع

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مدیریت آب و آبیاری
دوره 16، شماره 1
تیر 1405
صفحه 163-177

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