تخصیص بهینه آب و زمین در شبکه‌ی آبیاری مغان با ترکیب مدل‌سازی گیاهی و الگوریتم ژنتیک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران.

2 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی، مشهد، ایران

3 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

4 گروه مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران

5 گروه ریاضی کاربردی، دانشکده علوم ریاضی، دانشگاه فردوسی مشهد، مشهد، ایران.

چکیده

آب یکی از مهمترین عوامل تأمین امنیت غذایی جمعیت رو به رشد جهان است. محدودیت منابع آب کشور و همچنین رقابت فزاینده‌ی بخش‌های مختلف جهت استفاده از آب، مدیریت بهینه از منابع آبی را ضروری می‌سازد. در شبکه‌های آبیاری استراتژی‌های مختلفی برای مدیریت منابع آبی به‌کار گرفته می‌شود. یکی از این موارد، تخصیص بهینه آب و زمین است. در این پژوهش یک مدل بهینه‌ساز تخصیص آب و زمین با هدف بیشینه‌سازی سود اقتصادی، بر مبنای الگوریتم ژنتیک و استفاده از مدل گیاهی AquaCrop plug-in ارائه شده است. برای این منظور کدنویسی سی شارپ (C#) در فضای ویژوال استودیو انجام شد. برای سنجش کارایی مدل، اراضی تحت پوشش یکی از کانال‌های شبکه‌ی آبیاری مغان بررسی شد. در این مدل سال زراعی به 36 دوره ده روزه تقسیم و عمق آب آبیاری در هر دوره و مساحت زیر کشت نیز به‌عنوان متغیرهای تصمیم در گرفته شدند. نتایج نشان داد بیشترین درصد افزایش میزان سود اقتصادی مربوط به گیاه ذرت دانه‌ای کشت اول، یونجه و گندم به‌ترتیب با 9، 3/7 و 7 درصد است. این در حالی است که کمترین افزایش میزان سود اقتصادی مربوط به گیاه ذرت دانه‌ای کشت دوم و سویا است. حجم آب تخصیص یافته در حالت بهینه به میزان 7/14 درصد کاهش یافت، اما در مقابل سود اقتصادی با افزایش 7/5 درصدی همراه بود. لذا تخصیص بهینه آب در این منطقه بیش از افزایش سود اقتصادی، صرفه‌جویی در مصرف آب را تشویق می‌کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Optimal allocation of water and land in Moghan irrigation network using crop model and genetic algorithm

نویسندگان [English]

  • parisa kahkhamoghadam 1
  • Alinaghi Ziaei 2
  • kamran Davary 3
  • Amin Kanooni 4
  • Sedigheh Sadeghi 5
1 Water Engineering Department, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
2 Department Water Engineering, College Agriculture, Ferdowsi University, Mashhad, Iran
3 Water engineering, Agriculture faculty, Ferdowsi university of Mashhad, Mashhad, Iran
4 Water Engineering Department, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili
5 Department of Applied Mathematics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.
چکیده [English]

Water is one of the most important physical factors to provide the food security of the world’s growing population. The limitation of Iran’s water resources, as well as the increasing competition of different sectors for the use of water make the optimal management of water resources necessary. Different strategies are used in irrigation networks for water resources management. One of these strategies is optimal allocation of water and land. In this research, an optimization model for water and land allocation with the aim of maximizing economic benefit is presented based on genetic algorithm and using the AquaCrop plug-in model. For this purpose, #C coding was done in Visual Studio. In order to measure the model performance, the lands covered by one of the Moghan irrigation network channels were investigated. In this model, the agricultural year was divided into 36 periods of ten days. The irrigation water depth in each period and the cultivated area were considered as decision variables. The results show that the highest increase in percentage of economic benefit is related to the first-cultivation maize, alfalfa and wheat by 9, 7.3 and 7 percent respectively. Although the lowest increase in economic benefit is related to the second-cultivation seed maize and soybeans. The optimal allocated water volume was decreased by 14.7 percent, meanwhile the economic benefit was increased by 5.7 percent. Therefore, the optimal water allocation in this region encourages saving water consumption more than increasing economic benefit.

کلیدواژه‌ها [English]

  • AquaCrop Model
  • Economic Benefit
  • Genetic algorithm
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