توسعۀ مدل‌های ANN, FIS و ANFIS برای ارزیابی شاخص کفایت در سامانه‌های توزیع آب کشاورزی (مطالعۀ موردی: شبکۀ آبیاری رودشت)

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد منابع آب، گروه مهندسی آبیاری، پردیس ابوریحان، دانشگاه تهران

2 دانشیار، گروه مهندسی آبیاری، پردیس ابوریحان، دانشگاه تهران

چکیده

به‏منظور مدیریت صحیح آب در بخش کشاورزی، بهبود مدیریت بهره‏برداری سامانه‏های توزیع آب کشاورزی و ارزیابی آنها ضروری است. در تحقیق حاضر برای تحقق هدف یادشده، از مدل‏های سیستم استنتاج فازی (FIS)، شبکۀ عصبی (ANN) و سیستم استنتاج فازی-عصبی تطبیقی (ANFIS)، برای توسعۀ مدل هوشمند ارزیابی کفایت تحویل آب در یک کانال آبیاری، با در ‏نظر ‏گرفتن عدم قطعیت‏های موجود در فرایند بهره‏برداری استفاده شد. به‏منظور توسعه و بررسی چگونگی عملکرد مدل‏های توسعه داده‌شده، کانال اصلی شبکۀ آبیاری رودشت در استان اصفهان که با مشکل نوسان‌های شدید جریان ورودی رو‏به‏رو است، به عنوان مطالعۀ موردی انتخاب شد. از مدل شبیه‌ساز هیدرودینامیکی HEC-RAS، برای تولید اطلاعات مورد نیاز آموزش و صحت‏‌سنجی مدل‏های یادشده استفاده شد. نتایج نشان داد براساس شاخص آماری MAPE، ساختار‏های منتخب در دو مدل ANN و ANFIS در تخمین شاخص کفایت تحویل آب کشاورزی نسبت به مدل FIS، به‌ترتیب به مقدار 07/57، 68/56 درصد بهبود یافته است. بررسی نتایج نشان داد مدل‏های توسعه داده‌شدۀ هوشمند نسبت به روش‏های مرسوم ارزیابی (مدل‏های هیدرودینامیکی و شاخص‏های ارزیابی) نه‏تنها زمان‏بر نبوده بلکه با در نظر‏ گرفتن عدم قطعیت، نتایج دقیقی را ارائه می‏دهند. همچنین، مدل‏های ANN و ANFIS نسبت به FIS ‌عملکرد بهتری داشتند، بنابراین قابلیت استفاده برای سایر سامانه‏های توزیع آب کشاورزی را نیز دارند.

کلیدواژه‌ها


عنوان مقاله [English]

Development of ANN, FIS and ANFIS Models to Evaluate the Adequacy Index in Agricultural Water Distribution Systems (Case study: Rudasht Irrigation Network)

نویسندگان [English]

  • Habibeh Sharifi 1
  • Abbas Roozbahani 2
  • Mehdi Hashemy Shahdany 2
1 MSc Student of Water Resources Engineering, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran
2 Associate Professor, Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran
چکیده [English]

In order to properly manage water in the agricultural sector, it is necessary to improve the management of agricultural water distribution systems as well as their evaluation. In this research, to achieve this goal, the models of Fuzzy Inference System (FIS), Artificial Neural Network (ANN) and Adaptive Fuzzy Neural Inference System (ANFIS), to develop a smart model for analyzing the adequacy of water delivery in an irrigation canal, given uncertainty. In order to develop and evaluate the performance of the developed models, the main canal of Rudasht Irrigation Network in Isfahan province, which is facing the problem of severe fluctuations in the inflow, was selected as the case study. The HEC-RAS hydrodynamic simulator model was used to generate the information needed to train and validate these models. The results showed that according to the MAPE index, the selected structures in ANN and ANFIS models in estimating the adequacy index of agricultural water delivery compared to FIS model have improved by 57.07% and 56.68%, respectively. Evaluation of the results showed that the developed models compared to conventional evaluation methods (hydrodynamic models and evaluation indicators) not only did not take time but also provided more accurate results, considering the uncertainty and also, ANN and ANFIS models performed better than FIS, so they can be used for other agricultural water distribution systems.

کلیدواژه‌ها [English]

  • Agricultural Water Distribution
  • Delivery Adequacy
  • FIS
  • ANN
  • ANFIS
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