Investigating the effectiveness of data mining methods in predicting daily reference evapotranspiration (Case study: coastal strip stations in southern Iran)

Nonlinear relationships, inherent uncertainty, and the need for a lot of climate information in estimating evapotranspiration have made researchers use data-mining methods to estimate evapotranspiration in recent decades. The purpose of this research is to investigate the efficiency of data mining methods of support vector machine, decision tree, random forest and Gaussian process regression in forecasting the daily reference evapotranspiration of coastal strip stations in the south of the country. To do the work, daily reference evapotranspiration was calculated using 20year climatic data (2001-2021) using the Fao-Penman-Monteith method. Then, using these data as output data, 6 combined scenarios were evaluated based on the correlation between meteorological variables and reference evaporation-transpiration using data mining methods. The results of the investigations showed that all four data mining methods were able to estimate the reference evaporation-transpiration values in the studied areas.In all four stations, the Gaussian process regression method with the highest R2 value and the lowest RMSE and MAE values had a better estimate of the reference evapotranspiration values, and random forest, decision tree, and support vector machine methods were in the next ranks respectively. Gaussian process regression model in estimating reference evapotranspiration, this method is recommended for estimating reference evapotranspiration