بررسی کارایی روش‌های شبکه عصبی و رگرسیون چند متغیره در برآورد تابش کل خورشیدی در چند ایستگاه معرف اقلیم‌های خشک و نیمه‌خشک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی منابع آب و عضو انجمن پژوهشگران جوان، بخش مهندسی آب، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان

2 دانشیار، بخش مهندسی آب، دانشکده کشاورزی، دانشگاه شهید باهنر کرمان

چکیده

در این مطالعه قابلیت روش­های پرسپترون چند لایه (MLP) و رگرسیون خطی چند متغیره در برآورد شدت تابش کل خورشیدی مورد بررسی قرار گرفت. به این منظور از داده­های روزانه 25 ساله (2017-1992) شامل دمای حداکثر، میانگین دما، میانگین رطوبت نسبی، ساعات آفتابی و شدت تابش خورشیدی در پنج ایستگاه همدیدی بندرعباس، زنجان، شیراز، کرمان و مشهد استفاده شد. ورودی­های بکار رفته در مدل­ها شامل ترکیبات مختلفی از این متغیر­ها بودند. جهت بررسی عملکرد مدل­ها از آماره­های ضریب تعیین (R2)، ریشه میانگین مربعات خطا (RMSE)، میانگین مطلق خطا (MAE) و شاخص توافق (IA) استفاده شد. برای آموزش ساختار شبکه عصبی دو الگوریتم تنظیم بیزی (Br) و لونبرگ-مارکوات (LM) مورد مقایسه قرار گرفتند. علاوه بر این، فرآیند­های آموزش و اعتبارسنجی بر روی داده­ها انجام شد. نتایج مدل رگرسیون نشان داد که تمامی متغیرهای ورودی در ایستگاه­های بندرعباس، زنجان و شیراز بر تابش تأثیرگذارند، اما تأثیرگذاری رطوبت نسبی بر مقدار تابش در ایستگاه‌های کرمان و مشهد اندک بود. کاربرد ANN با دو الگوریتم نشان داد که ایستگاه­های بندرعباس و کرمان با الگوریتم Br و ایستگاه­های زنجان، شیراز و مشهد با الگوریتم LM نتایج بهتری  به دست می­دهند. با توجه به نتایج به دست آمده، کمترین مقادیر RMSE، MAE و بیشترین مقادیر IA و R2 مربوط به ایستگاه کرمان با اقلیم خشک سردسیر به ترتیب 799/2، 94/1، 954/0 و 838/0 می­باشد. در یک نتیجه­گیری کلی می­توان گفت که کارایی مدل شبکه عصبی در برآورد تابش خورشیدی نسبت به مدل رگرسیون خطی چند متغیره در مقایسه با داده­های مشاهداتی بهتر بوده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Performance evaluation of Neural Network and Multivariate Regression Methods for Estimation of Total Solar Radiation at several stations in Arid and Semi-arid Climates

نویسندگان [English]

  • Sedigheh Avazpour 1
  • Bahram Bakhtiari 2
  • Kourosh Qaderi 2
1 1. M. Sc. Student in Water Resources Engineering and member of Young Researchers Society, Water Engineering Department, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
2 Associate Professor, Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman,
چکیده [English]

In this study, the capability of multi-layer perceptron (MLP) and multivariate linear regression methods were evaluated to estimate the total solar radiation. For this purpose, the daily weather data of 25 years (1992-2017) including maximum temperature, mean temperature, relative humidity, sunshine hours and solar radiation were used in the five synoptic stations (Bandarabbas, Zanjan, Shiraz, Kerman and Mashhad). The inputs used in the models included various combinations of these variables, and the output was the solar radiation. To evaluate the performance of these models, Determination of Coefficient (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Index of Agreement (IA) were used. In order to train the structure of the ANN, two Bayesian-regularization (Br) and Levenberg-Marquardt (LM) algorithms were compared. Moreover, the training and validation processes were performed. The results of regression model showed that all the input variables are effective on the solar radiation estimation at Bandarabbas, Zanjan and Shiraz, but the effect of relative humidity on radiation at Kerman and Mashhad stations was low. The ANN application with two algorithms showed that Bandarabbas and Kerman stations using the Br algorithm and Zanjan, Shiraz and Mashhad using the LM algorithm give a good result. The lowest values of RMSE, MAE and the highest value of IA and R2 related to Kerman station were 2.799, 0.94, 0.954 and 0.838, respectively. As a main result, the comparison between computation and observation data showed that the ANN model gives better results than the linear regression model for estimation of radiation.

کلیدواژه‌ها [English]

  • Feed forward back propagation
  • Solar radiation
  • Radiation Modeling
  • Linear Correlation

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تحقیقات آب و خاک ایران
دوره 50، شماره 8
دی 1398
صفحه 1855-1869

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