پیش‌بینی تراز سطح آب مخزن سد با استفاده از روش ماشین هوشمند نظارت شده، مطالعه موردی: سد امیرکبیر کرج

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشیار /گروه مهندسی عمران، دانشکده فنی، دانشگاه شهرکرد

2 دانش‌آموخته کارشناسی ارشد / مهندسی منابع آب و سازه های هیدرولیکی، دانشکده فنی، دانشگاه شهرکرد.

چکیده

پیش‌بینی صحیح تغییرات تراز سطح آب مخازن به عنوان یکی از مسائل مهم جهت مدیریت، طراحی، بهره‌برداری از سدها و تأمین نیازهای آبی مطرح می‌باشد. در این مطالعه بر پایه پنج مدل نرم رگرسیون بردار پشتیبان (SVR)، سیستم استنتاج عصبی-فازی تطبیقی (ANFIS)، شبکه عصبی (ANN)، شبکه عصبی شعاعی (RBFNN) و شبکه عصبی مبتنی بر رگرسیون عمومی (GRNN) و استفاده تلفیقی از نتایج آن‌ها به عنوان ورودی به یکی از این پنج مدل، ساختاری تحت عنوان ماشین هوشمند نظارت شده (SICM) جهت برآورد تراز سطح آب ماهانه مخزن سد امیرکبیر کرج پیشنهاد گردید. داده‌های مورد استفاده شامل تراز سطح آب، بارندگی، تبخیر، حجم ورودی و خروجی از مخزن سد بوده و ارزیابی مدل‌های مذکور توسط نه شاخص خطا صورت گرفت و با استفاده از روش تصمیم‌گیرنده ویکور، بهترین مدل از میان مدل‌های مذکور انتخاب گردید. پس از انجام بررسی‌های لازم در میان مدل‌های نرم مورد استفاده، مدل ANN با ضریب راندمان نش و میانگین مجذور خطای به ترتیب 89/0 و 37/23 متر مربع به عنوان بهترین مدل شناخته شد. نتایج بدست آمده از رویکرد پیشنهادی نشان می‌دهد که مدل نظارت شده (هیبریدی) شبکه عصبی (SICM-ANN) با افزایش ضریب راندمان نش به 94/0 و کاهش میانگین مجذور خطا به 85/12 متر مربع (بیش از 45 درصد کاهش) توانسته عملکرد بالایی را در پیش‌بینی صحیح میزان تراز سطح آب ماهانه مخزن سد کرج ارائه نماید. بر این اساس استفاده هیبریدی از مدل‌های نرم می‌تواند در کاهش چشمگیر خطای پیش‌بینی تراز سطح آب نسبت به مدل‌های منفرد به طور مؤثری بکار گرفته شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Reservoir Water Level Prediction Using Supervised Intelligent Committee Machine Method, Case Study: Karaj Amirkabir Dam

نویسندگان [English]

  • M. Mohammad Rezapour Tabari 1
  • M.M. Malekpour Shahraki 2
1 Associate Professor, Deptartment of Engineering, Shahrekord University, Shahrekord, Iran.
2 M.Sc. Graduate of Civil Engineering - Hydraulic Structures, Shahrekord University, Shahrekord, Iran
چکیده [English]

The proper prediction of reservoirs water level variation is considered as one of the important issues for management, designing, operation of dams and water supply. In this study, based on five soft models such as support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and the combined use of their results as input to one of these five models, a new structure called supervised intelligent committee machine (SICM) was proposed to predict the monthly reservoir water level of Karaj Amirkabir dam. The data used in this paper are water level, precipitation, evaporation, inflow and outflow of the dam. The evaluation of these models was done by nine error indexes and also the best model between them was selected using vikor decision maker method. After performing the necessary evaluations among the used soft models, the ANN known as the best model with nash–sutcliffe efficiency (NS) and mean square error (MSE) equal to 0.89 and 23.37 square meters, respectively. The results of the proposed approach are shown that the supervised (hybrid) neural network (SICM-ANN) has been able to provide high performance in predicting the monthly reservoir water level in Karaj dam with increasing the NS coefficient to 0.94 and decreasing the MSE index to 12.85 square meters (more than 45 percent decrease). Accordingly, hybrid use of soft models can be effectively applied to significantly reduce the predicted error of water level rather than single models.

کلیدواژه‌ها [English]

  • prediction
  • Reservoir water level
  • Karaj Amirkabir dam
  • Supervised intelligent committee machine
  • Soft models

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