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تعیین متغیرهای ورودی برای تخمین تابش خورشیدی با استفاده از تئوری آنتروپی و تحلیل مؤلفه اصلی | ||
| تحقیقات آب و خاک ایران | ||
| مقاله 10، دوره 50، شماره 3، مرداد 1398، صفحه 625-639 اصل مقاله (1.76 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2018.257150.667906 | ||
| نویسندگان | ||
| بابک محمدی1؛ زهرا آقاشریعتمداری* 2؛ روزبه مؤذن زاده3 | ||
| 1گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران کرج، ایران | ||
| 2هیئت علمی/پردیس کشاورزی ومنابع طبیعی | ||
| 3استادیار گروه آب و خاک/ دانشکده کشاورزی/دانشگاه شاهرود/ شاهرود/ایران | ||
| چکیده | ||
| تابش خورشیدی رسیده به سطح زمین یکی از متغیرهای اصلی مورد استفاده در پروژه‌ها و مدلسازیهای هیدرولوژی، کشاورزی، هواشناسی و اقلیمی میباشد. در این تحقیق قابلیت عملکرد روش تحلیل مؤلفه اصلی (PCA) و تئوری آنتروپی (EN) برای تعیین ورودی مدلهای شبکه عصبی مصنوعی پرسپترون چندلایه (MLP)، شبکه عصبی مصنوعی تابع پایه شعاعی (RBF)، ماشین بردار پشتیبان (SVM) و برنامه‌ریزی ژنتیک (GEP) در برآورد تابش خورشیدی در دو ایستگاه همدید کرمان و مشهد به ترتیب در حد فاصل سال‌های 1984 تا 2005 و 1980 تا 2004 مورد بررسی قرار گرفت. متغیرهای میانگین دما، میانگین کمبود فشار بخار آب اشباع، دمای کمینه، دمای بیشینه، ساعت آفتابی، رطوبت نسبی، دمای نقطه شبنم، فشار بخار ساعتی، دید افقی و محتوی بخار آب جو به عنوان ورودی روشهای پیشپردازش انتخاب شدند. با توجه به نتایج بهدست آمده در ایستگاه کرمان، مدل ENT-MLP با ریشه میانگین مربعات خطای برابر36/38 (Mj/m2) و ضریب تبیین 93/0 R2= بهترین عملکرد را داشته است. همچنین در ایستگاه مشهد مدل PCA-MLP با ریشه میانگین مربعات خطای برابر75/79 (Mj/m2) و ضریب تبیین 77/0 R2= بهترین عملکرد را داشته است. به طور کلی هر دو روش پیشپردازش تحلیل مؤلفه اصلی (PCA) و تئوری آنتروپی (EN) برای تعیین ورودی مدلهای تخمینگر به منظور تخمین تابش خورشیدی روش مناسبی تشخیص داده شدند. | ||
| کلیدواژه‌ها | ||
| پیش پردازش داده‌ها؛ تابش خورشیدی؛ تحلیل مؤلفه اصلی؛ تئوری آنتروپی؛ شبکه عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Determination of Input Variables to Estimate Solar Radiation Using Entropy Theory and Principal Component Analysis | ||
| نویسندگان [English] | ||
| Babak Mohammadi1؛ Zahra Aghashariatmadari2؛ roozbeh moazenzadeh3 | ||
| 1Irrigation & Reclamation Engrg. Dept. University of Tehran Karaj, Iran. | ||
| 2Assistant Prof., Irrigation & Reclamation Engrg. Dept. University of Tehran Karaj, Iran. Tel/Fax:+98-263-2241119 | ||
| 3Assistant professor, Soil and Water Department, Faculty of Agriculture, Shahrood University of Technology, Shahrood, Iran | ||
| چکیده [English] | ||
| Solar radiation arriving to the land surface is one of the major variables that is used in projects and hydrological, agricultural, meteorological and climatic models. In this study, the functionality of the principal component analysis (PCA) and the entropy theory (EN) for determination of inputs to multilevel perceptron artificial neural network (MLP), artificial neural network, radial basis function (RBF), support vector machine (SVM)and genetic programming (GEP), was investigated for estimation of solar radiation at two stations (Kerman and Mashhad) during 1984-2005 and 1980-2004 periods, respectively. The average temperature, mean water deficit pressure, minimum temperature, maximum temperature, sunshine, relative humidity, dew point temperature, hourly vapor pressure, horizontal visibility and water content were selected as inputs of pre-processing methods. The obtained results in Kerman station showed that the ENT-MLP model with RMSE=38.36 (Mj /m2) and R2 = 0.93 have had the best performance. Also in Mashhad station, PCA-MLP model with RMSE=79.75 (Mj / m2) and R2 = 0.77 had the best performance. In general, the both pre-processing principal component analysis and entropy theory were recognized as the proper methods for determination of estimating models input to estimate solar radiation. | ||
| کلیدواژه‌ها [English] | ||
| Data Preprocessing, Solar radiation, Principal component analysis, Entropy Theory, Artificial Neural Network | ||
| مراجع | ||
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