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

Abstract 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.

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