پیش‌بینی نوسانات ماهانه سطح آب دریاچه ارومیه با استفاده از مدل هوش مصنوعی مرکب نظارت‌شده

نویسندگان

1 دانشجوی دکتری هیدروژئولوژی، دانشکده علوم طبیعی، دانشگاه تبریز

2 استاد گروه علوم زمین، دانشکده علوم طبیعی، دانشگاه تبریز

3 استادیار دانشکده زمین‌شناسی، پردیس علوم، دانشگاه تهران

چکیده

در سال­های اخیر کاهش سطح آب دریاچه ارومیه سبب ایجاد بحران آبی و زیست­محیطی در منطقه گردیده است. بنابراین ضروری است که مدیریت و برنامه­ریزی صحیح و قابل­اعتماد در این زمینه صورت گیرد که لازمه آن مدل­سازی سطح آب دریاچه برای آینده می­باشد. در این تحقیق از روش­های شبکه عصبی مصنوعی (ANN)، سیستم استنتاج فازی- عصبی تطبیقی (ANFIS) و ماشین بردار پشتیبان (SVM) برای پیش­بینی سطح آب یک [h (t+1)]، دو [h (t+2)] و سه  [h (t+3)] ماه آینده دریاچه ارومیه استفاده گردید و در نهایت از یک مدل هوش مصنوعی مرکب نظارت شده (SCMAI) برای رسیدن به یک عملکرد بهتر از مدل­های منفرد به­کار گرفته شده، استفاده شد. برای مدل­سازی، اطلاعات سطح آب ماه جاری  [h (t)]و یازده ماه گذشته [h (t-1),…, h (t-11)] به­عنوان ورودی و سطح آب یک، دو و سه ماه آینده به­عنوان خروجی مدل­ها در نظر گرفته شدند. داده­ها به دو دسته داده­های آموزش/ اعتبارسنجی (90 درصد کل داده­ها) و داده­های آزمایش (10 درصد کل داده­ها) تقسیم­بندی گردیدند و پس از مدل­سازی، عملکرد مدل­ها بر اساس پارامترهای ضریب تعیین (R2)، جذر میانگین مربعات خطا (RMSE) و میانگین مطلق خطا (MAE) ارزیابی شدند. نتایج نشان دادند که مدل­های بردار پشتیبان عملکرد بهتری نسبت به مدل­های شبکه عصبی مصنوعی و عصبی- فازی دارند. مدل هوش مصنوعی مرکب نظارت شده به­منظور ترکیب نتایج مدل­های مختلف به­کار گرفته شد و نشان داد که مدل­های هوش مصنوعی مرکب نظارت شده قادرند کارایی مدل­های منفرد هوش مصنوعی را بهبود بخشند. نتایج معیارهای عملکرد مدل­ هوش مصنوعی مرکب نظارت شده بیان می­کند که مدل­سازی یک ماه آینده سطح آب با مقادیر R2، RMSE و MAE به­ترتیب برابر با 9896/0، 0547/0 متر و 0421/0 متر در مقایسه با مدل بردار پشتیبان عملکرد بهتری دارد که این عملکرد برای پیش­بینی­های دو و سه ماه آینده سطح آب دریاچه نیز صادق می­باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Forecasting Monthly Water Level Fluctuations of Lake Urmia Using Supervised Committee Machine Artificial Intelligence Model

نویسندگان [English]

  • R Barzegar 1
  • A Asghari Moghaddam 2
  • E Fijani 3
1 Ph.D student of Hydrogeology, Dept. of Earth Sciences, Faculty of Natural Sciences, Univ. of Tabriz, Iran
2 Prof., Dept. of Earth Sciences, Faculty of Natural Sciences. Univ. of Tabriz, Iran
3 Assist. Prof., School of Geology, University College of Science, University of Tehran, Tehran, Iran
چکیده [English]

In recent years, declining the water level of Lake Urmia has caused water and environmental crisis in the area. Therefore, it is urgent to carry out an accurate and reliable management and planning which requires modeling the lake's water level for the future. In this research, the artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) models were used to forecast the Lake Urmia water level fluctuations for one, two and three months ahead forecast horizons and finally, a supervised committee machine artificial intelligence (SCMAI) model was used to obtain a better performance than the used individual models. To develop the models, the current month [h (t)] and eleven months water level lags [h (t-1),…, h (t-11)] were introduced as input variables to forecast one, two and three steps ahead water levels. The datasets were divided into two subsets of training/validation (90%) and testing (10%). The performances of the models were evaluated based on the coefficient of determination (R2), the root mean square error (RMSE) and the mean absolute error (MAE). The results showed that the SVM models had better performance than the ANN and ANFIS models. The SCMAI model was applied to combine the used models’ outputs and illustrated that the SCMAI models are able to improve the performance of the individual artificial intelligence models. The results of the performance criteria for SCMAI model indicated that the one month step ahead water level modeling with R2, RMSE and MAE equal to 0.9896, 0.0547 m and 0.0421 m, respectively outperformed in comparison with SVM model which this performance is reliable for the two and three months step ahead lake's water level.

کلیدواژه‌ها [English]

  • Forecasting
  • Supervised committee machine artificial intelligence
  • Support Vector Machine
  • Lake Urmia
  • Water level
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