مقایسه مدل‌های هوشمند در پیش‌بینی نوسانات تراز سطح آب دریاچه زریوار با درنظرگیری تراز آب زیرزمینی

نوع مقاله : یادداشت فنی (5 صفحه)

نویسندگان

1 دانشجو/پردیس ابوریحان - دانشگاه تهران

2 دانشگاه تهران - پردیس ابوریحان

3 دانشیار / دانشگاه تهران

4 استادیار/ گروه مهندسی منابع آب دانشگاه کردستان

چکیده

همواره پیش‌بینی سطح آب دریاچه‌ها در سطح دنیا از مهم‌ترین و پیچیده‌ترین فرایندهای هیدرولوژیکی است که برآورد آن می‌تواند در راستای جلوگیری از بروز وضعیت نامطلوب و مدیریت صحیح این اکوسیستم ارزشمند بکار گرفته شود. از اینرو در این پژوهش از چهار تکنیک محاسبات نرم موجک-شبکه عصبی مصنوعی (WANN)، شبکه عصبی مصنوعی (ANN)، مدل استنتاج عصبی-فازی تطبیقی (ANFIS) و برنامه‌ریزی بیان ژن (GEP) در محاسبه مقادیر پیش‌بینی شده دو ماه آینده تراز سطح آب دریاچه زریوار استفاده شد. نتایج سری زمانی پیش‌بینی با استفاده از نمودارهای سری زمانی پیش‌بینی شده توسط انواع مدل‌های هوشمند و همچنین شاخص‌های آماری RMSE، R2 و MAE مقایسه شدند. نتایج این تحقیق نشان داد از چهار مدل مذکور به صورت قابل ملاحظه‌ای عملکرد مدل WANN از مدل‌های دیگر در پیش بینی سطح آب دریاچه بهتر بود. پس از مدل WANN به لحاظ صحت مقادیر شبیه‌سازی شده به ترتیب مدل‌های ANFIS، GEP و ANN تعیین شدند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison of intelligent models to predict water level fluctuations of Zarival Lake using groundwater level

نویسندگان [English]

  • Siavash Gavili 1
  • Saman Javadi 2
  • M. E Banihabib 3
  • Hadi Sanikhani 4
3 Associate professor, University of Tehran
4 Department of Water engineering, University of Kordestan
چکیده [English]

In recent decades, drought and lack of water resources management has caused many lakes and wetlands to be in critical conditions. Surface water level prediction is an important and complex hydrological process but it is required for better management and improvement of their ecosystem. In this research, four soft-computing techniques including wavelet artificial neural network (WANN), artificial neural network (ANN), adaptive-neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) were used to predict 2-month water level fluctuations of Zarivar Lake. The predicted water levels in each technique were compared with observed data and statistical indicators, RMSE, MAE and R2 were used to evaluate the performance of each method. The results proved that WANN performed considerably better and its prediction was more accurate. After WANN, the accuracy of ANFIS, GEO and ANN, respectively, were better and closer to observed data. The selected technique in this research can be recommended to predict the water levels in lakes and wetlands with enough accuracy.

کلیدواژه‌ها [English]

  • Soft Computing
  • Zarivar lake
  • predicting water level
  • wavelet-neural network model
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