آنالیز حساسیت داده‌های ورودی به شبکه عصبی مصنوعی به‌منظور برآورد مقدار تبخیر روزانه

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشیار گروه مهندسی عمران آب، دانشکده مهندسی عمران، دانشگاه تبریز

2 دانشجوی کارشناسی ارشد سازه های هیدرولیکی، دانشکده مهندسی عمران، دانشگاه تبریز

چکیده

برآورد مقادیر تبخیر برای مدیریت کارآمد منابع آب در مناطق با اقلیم نیمه خشک امری ضروری است. این مقاله کاربرد شبکه‌های عصبی مصنوعی، رگرسیون خطی چند متغیره و سه روش تجربی تعادل انرژی، آیرودینامیکی و ترکیبی پنمن را به‌منظور برآورد مقادیر تبخیر روزانه تشتک برای دو شهر تبریز و ارومیه مورد ارزیابی قرار داد. علاوه بر این آنالیز حساسیت به دو روش مشتقات جزئی و وزن‌های اتصالی برای تبیین میزان اثر هر پارامتر ورودی از نظر مقدار و جهت روی نتایج مدل‌های شبکه عصبی و نیز تشخیص بهترین ترکیبات ورودی انجام شد. متغیرهای هیدرولوژیکی استفاده شده شامل مشاهدات روزانه کمّیتهای دمای هوا، تبخیر تشتک، تشعشع خورشید، فشار هوا، رطوبت نسبی و سرعت باد بودند. نتایج روشهای کلاسیک و مدل‌های شبکه عصبی با مقادیر تبخیر روزانه مقایسه شد. مقایسه‌ها بهتر بودن پاسخ شبکه‌های عصبی در برآورد تبخیر روزانه نسبت به سایر مدل‌ها را نشان داد. نتایج آنالیز حساسیت نشان داد که درجه حرارت هوا، تشعشع خورشیدی و تبخیر روز گذشته بیشترین تأثیر را روی تبخیر روزانه نسبت به سایر عوامل در هر دو منطقه دارند و تأثیر متغیرهای دیگر کم است.

کلیدواژه‌ها


عنوان مقاله [English]

Sensitivity Analysis of ANN Inputs in Estimating Daily Evaporation

نویسندگان [English]

  • Vahid Nourani 1
  • Mina Sayyahfard 2
چکیده [English]

Estimation of evaporation values is needed for efficient management of water resources at semi-arid regions. This paper presents application of Artificial Neural Networks (ANNs), Multiple Linear Regression (MLR) and empirical models viz.: Energy balance ، Aerodynamic ، Penman for estimation of daily pan evaporation for Tabriz and Urmia cities. Furthermore, in order to determine the effect of each input parameter on the output variable in terms of magnitude and direction and also identify the best combinations of the model inputs, two sensitivity analysis methods i.e. the Partial Derivation method (PaD) and the Weights method have been applied on the ANNs results. The used hydrological variables include daily observations of air temperature, pan evaporation, solar radiation, air pressure, relative humidity, and wind speed. The results of the classic methods and ANN models are compared to daily observations of evaporation values. The comparison showed that there is better agreement between the ANN estimations and measurements of daily evaporation than other models.  Sensitivity analysis results showed that air temperature, solar radiation and previous day evaporation have maximum effects on daily evaporation in both regions and the contributions of the other variables are insignificant.

کلیدواژه‌ها [English]

  • Sensitivity analysis
  • Artificial Neural Networks
  • Daily Evaporation
  • Tabriz
  • Urmia
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