Abstract:
Evaporation is one of the vital components of hydrological cycle. Precise estimation of pan evaporation (Epan) is essential for the
sustainable water resources management. The current study proposed a novel approach to estimate daily Epan across the humid
region of Iran using support vector regression (SVR) technique coupled with Krill Herd Algorithm (SVR-KHA). Meteorological
data were collected from three stations (Bandar Abbas, Rudsar, and Osku) over a period from 2008 to 2018 and used for
application. Of the data, 70% were used for training and remaining 30% were used for testing. The study considered seven
different combinations of input variables for predicting daily Epan at each station. The influence of KHA hybridization is
examined by comparing results of SVR-KHA algorithm with simple SVR through a multitude of statistical performance
evaluation criteria such as coefficient of determination (R2
), Wilmot’s index (WI), root-mean-square error (RMSE), Mean
Absolute Error (MAE), Relative Root Mean Square Error (RRMSE), Mean Absolute Relative Error (MARE), and several
graphical tools. Single input SVR1 model hybrid with KHA (SVR-KHA1) showed improved performance (R2 of 0.717 and
RMSE of 1.032 mm/day) as compared with multi-input SVR models, e.g., SVR5 (with RMSE and MAE of 1.037 mm/day and
0.773 mm/day), while SVR7 model hybridized with KHA (SVR-KHA7), which considers seven meteorological variables as
input, performed best as compared with other models considered in this study. Epan estimates at Bandar Abbas and Rudsar by
SVR and SVR-KHA are similar (with R2 statistics values of 0.82 and 0.84 at Bandar Abbas station, and 0.88 and 0.9 at Rudsar
station, respectively). However, better improvements in Epan estimates are observed at Osku station (with R2 of 0.91 and 0.86,
respectively), which is situated at interior geographical location with a higher altitude than the other two coastal stations. Overall,
the results showed consistent performance of SVR-KHA model with stable residuals of lower magnitude as compared with
standalone SVR models.
