نوع مقاله : مقاله پژوهشی
نویسندگان
1
دانشجوی دکتری آبیاری و زهکشی، دانشکده کشاورزی، دانشگاه بیرجند، بیرجند، ایران.
2
دانش آموخته کارشناسیارشد سازه های آبی، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران.
3
دانشیار گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بی رجند، بیرجند، ایران.
10.22077/jaaq.2025.8597.1086
چکیده
کاهش نزولات جوی و برداشت بیرویه از آبهای زیرزمینی در دهههای اخیر، به ویژه در مناطق خشک و نیمهخشک مانند شهرستان بیرجند در استان خراسان جنوبی، منجر به افت شدید سطح آب زیرزمینی و کاهش دبی قناتها شده است. با توجه به اینکه شهرستان بیرجند با داشتن بیش از 1875 رشته قنات و تخلیه 23 میلیون مترمکعب در سال، بیشترین تعداد قناتها را در سطح استان دارد و بیش از 90 درصد آب مصرفی در این شهرستان از طریق قناتها تأمین میشود، پیشبینی دقیق دبی آنها از اهمیت حیاتی برخوردار است. در این پژوهش، از شبکه عصبی فازی تطبیقی (ANFIS) به عنوان یک ابزار قدرتمند برای مدلسازی سیستمهای پیچیده و غیرخطی استفاده شده است. این مدل قادر است روابط پیچیده بین متغیرهای ورودی (مانند بارندگی، تبخیر، سطح آب زیرزمینی) و خروجی (دبی قنات) را شناسایی کرده و پیشبینی دقیقی از دبی آینده ارائه دهد. نتایج حاصل از این پژوهش نشان میدهد که مدل ANFIS با ضریب همبستگی 0/98، ضریب نش - ساتکلیف 0/97 و میانگین مربعات خطا 0/049، در مقایسه با سایر مدلها با دقت بسیار بالایی قادر به پیشبینی دبی قناتها است و میتواند در تصمیمگیریهای مرتبط با مدیریت پایدار منابع آب زیرزمینی در منطقه مورد مطالعه مورد استفاده قرار گیرد.
کلیدواژهها
موضوعات
عنوان مقاله [English]
Evaluation of the accuracy of fuzzy neural network in estimating the discharge of Qanats in Birjand city
نویسندگان [English]
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Amir Khayat
1
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Zahra Akhondi
2
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Hossein Khozeymehnezhad
3
1
PhD student of Irrigation and Drainage, University of Birjand, Birjand, Iran.
2
Master of Science in Hydraulic Structures, University of Zabol, Zabol, Iran.
3
Associate Professor, Department of Water Science and Engineering, University of Birjand, Birjand, Iran.
چکیده [English]
The decline in precipitation and excessive groundwater extraction in recent decades, particularly in arid and semi-arid regions such as Birjand County in South Khorasan Province, has resulted in a significant decrease in groundwater levels and a reduction in qanat flow rates. Given that Birjand County possesses over 1875 qanats, contributing to a total discharge of 23 million cubic meters annually, and relies on qanats for more than 90% of its water consumption, accurate prediction of their flow rates is of paramount importance. In this research, an adaptive neuro-fuzzy inference system (ANFIS) has been employed as a powerful tool for modeling intricate and nonlinear systems. This model is capable of identifying the complex relationships between input variables such as rainfall, evaporation, and groundwater level, and the output variable, which is the qanat flow rate. The model's capability enables it to deliver precise predictions of future flow rates.
The findings of this study demonstrate that the ANFIS model outperformed other models, achieving a correlation coefficient of 0.98, a Nash-Sutcliffe efficiency of 0.97, and a mean squared error of 0.049. This exceptional accuracy underscores the model's potential for predicting qanat flow rates. Consequently, this model can be effectively utilized in decision-making processes related to the sustainable management of groundwater resources within the study area.
کلیدواژهها [English]
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Qanats
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Water Resources Management
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Fuzzy Neural Network
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Birjand City
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