دانلود مقاله انگلیسی رایگان:داده کاوی هواشناسی و مدل های هشمند-داده ترکیبی برای تبخیر مرجع شبیه سازی : یک مطالعه موردی در عراق - 2019
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  • Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq
    Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq


    ترجمه فارسی عنوان مقاله:

    داده کاوی هواشناسی و مدل های هشمند-داده ترکیبی برای تبخیر مرجع شبیه سازی : یک مطالعه موردی در عراق


    منبع:

    Sciencedirect - Elsevier - Computers and Electronics in Agriculture, 167 (2019) 105041: doi:10:1016/j:compag:2019:105041


    نویسنده:

    Khabat Khosravia, Prasad Daggupatia, Mohammad Taghi Alamib, Salih Muhammad Awadhc, Mazen Ismaeel Gharebd, Mehdi Panahii,j, Binh Thai Phamf,⁎, Fatemeh Rezaiee, Chongchong Qig, Zaher Mundher Yaseenh


    چکیده انگلیسی:

    To model an agriculture process for any region, it is significantly essential to accurately simulate the reference evaporation (ETo) from the available regional meteorological parameters. Nine models, including five data mining algorithms and four adaptive neuro-fuzzy inference systems (ANFISs), were tested for their ability to predict ETo at meteorological stations in Baghdad and Mosul (Iraq). Various weather parameters (e.g., wind speed, sunshine hours, rainfall, maximum and minimum temperature and relative humidity) were recorded and employed as explanatory variables in the models. Pearson correlation analysis showed ETo to have the closest correlation with sunshine hours, maximum and minimum temperatures, and relative humidity. The modeling performance was assessed using the statistical measures of coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), percentage of bias (PBIAS), and the ratio of RMSE to the standard deviation of observations (RSR). Investigations on the modeling accuracy with different input parameter combinations showed that, despite the different structures of the models, no single input combination showed a consistent modeling outcome. Fewer variables were necessary to achieve the same high predictive power for the models developed for the Baghdad station than for those developed for the Mosul station. For both stations, the ANFIS-GA model generally showed the greatest predictive power whereas the random tree algorithm showed the poorest. Moreover, hybrid models showed a higher predictive power than the individual models.
    Keywords: Evaporation rate prediction | Data mining | Bio-inspired ANFIS model | Arid and semi-arid climatic | Iraq region


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 16
    حجم فایل: 4535 کیلوبایت

    قیمت: رایگان


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