پیش‌بینی تبخیر ماهانه به کمک مدل‌های خطی و غیرخطی سری زمانی (مطالعه موردی: ایستگاه تبخیرسنجی سد اکباتان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران.

2 دانش آموخته دکتری مهندسی منابع آب، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران.

3 دانشجوی کارشناسی ارشد مهندسی منابع آب، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران.

چکیده

پیش‌بینی تبخیر به­عنوان یک جزء اصلی چرخه هیدرولوژیکی، اهمیت زیادی در مطالعات هواشناسی و منابع آب دارد. در این پژوهش، کارایی مدل‌های ARIMA، SARIMA، برنامه‌ریزی بیان ژن، رگرسیون خطی چندگانه، مونت کارلو و توماس فیرینگ در پیش‌بینی مقادیر ماهانه تبخیراز تشت بررسی گردید. بدین منظور، داده‌های تبخیر ماهانه ایستگاه تبخیرسنجی سد اکباتان در یک دوره 47 ساله (1396-1350) مورد استفاده قرار گرفتند. از آمار دوره 40 ساله 1389-1350 برای واسنجی و از داده های سالهای 1396-1390 جهت  اعتبارسنجی مدلها نتایج استفاده گردید. معیارهای ارزیابی ضریب تبیین، ریشه میانگین مربعات خطا، خطای استاندارد، معیار اطلاعاتی آکائیک و ضریب نش- ساتکلیف برای ارزیابی و مقایسه عملکرد مدل‌ها مورد استفاده قرار گرفت. نتایج نشان داد که مدل‌ SARIMA عملکرد دقیق‌تری در پیش­بینی تبخیر ماهانه داشته و مدل‌های برنامه‌ریزی بیان ژن، ARIMA و رگرسیون خطی چندگانه به ترتیب در رتبه‌های دوم تا چهارم قرار دارند. با توجه به این که مدل برنامه‌ریزی بیان ژن از سهولت کاربست بیشتر و تعداد پارامتر کمتری نسبت به مدل SARIMA برخوردار است ، پیش‌بینی را آسان تر و در زمان کمتری انجام می‌دهد و در میان روشهای مورد استفاده قابل توصیه است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Predicting monthly evaporation using linear and nonlinear time series models (Case study: Ekbatan Dam station)

نویسندگان [English]

  • Hamed Nozari 1
  • Saeed Azadi 2
  • Nadia Sedghnejad 3
  • Sajjad Pouyanfar 3
1 Dept. of Water science engineering, Faculty of Agriculture, Bu-Ali Sina Univ., Hamedan, Iran.
2 Dept. of Water science engineering, Faculty of Agriculture, Bu-Ali Sina Univ., Hamedan, Iran.
3 Dept. of Water science engineering, Faculty of Agriculture, Bu-Ali Sina Univ., Hamedan, Iran.
چکیده [English]

Prediction of evaporation as a key component of the hydrological cycle is one of the most important issues in water resources management and meteorology studies. In this study, the performance of ARIMA, SARIMA, gene expression programming, multiple linear regression, Monte Carlo and Thomas Fairing models in prediction of monthly evaporation values of Ekbatan Dam station, west of Iran in a 47 years period (1971-2017) were evaluated. For calibration of these models, 40 years data (1971-2010), and for validation, data from 2011-2017 (7-year) were used. The statistical metrics of the correlation coefficient, root mean square error, standard error, the Akaike information criterion, and NSE were selected for evaluation and comparison of models. The results showed that the SARIMA model has more accurate performance in predicting monthly evaporation. The GEP model, ARIMA, and MLR are ranked second to fourth. However, since the GEP model is easier to use than the SARIMA model and requires fewer variables than the SARIMA model, it shows promise to generate faster results, therefore, the GEP models can be the preferred option compared to others.

کلیدواژه‌ها [English]

  • Artificial Intelligence
  • Ekbatan Dam
  • Evaporation
  • Simulation
  • Time Series
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