کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6540752 | 158863 | 2015 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology
ترجمه فارسی عنوان
تعیین پارامترهای موثر بر آب و هوا در تبخیر تعرق مرجع توسط روش انعطاف پذیری عصبی-فازی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
The adaptive neuro-fuzzy inference system (ANFIS) is applied for selection of the most influential reference evapotranspiration (ET0) parameters. This procedure is typically called variable selection. It is identical to finding a subset of the full set of recorded variables that illustrates good predictive abilities. The full weather datasets for seven meteorological parameters were obtained from twelve weather stations in Serbia during the period 1980-2010. The monthly ET0 data are obtained by the Penman-Monteith method, which is proposed by Food and Agriculture Organization of the United Nations as the standard method for the estimation of ET0. As the performance evaluation criteria of the ANFIS models the following statistical indicators were used: the root mean squared error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R2). Sunshine hours are the most influential single parameter for ET0 estimation (RMSEÂ =Â 0.4398Â mm/day). The obtained results indicate that among the input variables sunshine hours, actual vapor pressure and minimum air temperature, are the most influential for ET0 estimation. The maximum relative humidity and maximum air temperature are the most influential optimal combination of two parameters (RMSEÂ =Â 0.2583Â mm/day).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 114, June 2015, Pages 277-284
Journal: Computers and Electronics in Agriculture - Volume 114, June 2015, Pages 277-284
نویسندگان
Dalibor PetkoviÄ, Milan Gocic, Slavisa Trajkovic, Shahaboddin Shamshirband, Shervin Motamedi, Roslan Hashim, Hossein Bonakdari,