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