Predicting Daily Pan Evaporation (Epan) from Dam Reservoirs in the Mediterranean Regions of Algeria: OPELM vs OSELM

In the present study, we propose the application of two artificial intelligence models, namely: (i) the optimally pruned extreme learning machine (OPELM); and (ii) the online sequential extreme learning machine (OSELM) models, for estimating daily pan evaporation (E-pan). The two models were developed and compared using four climatic data collected at two stations: Ain Dalia and Zit Emba. The maximum and minimum temperatures (T-max, T-min), wind speed (U-2), relative humidity (RH %) and E-pan data were used as inputs to the models. Pan evaporation E-pan was directly measured using Class A evaporation pan. The results show that the two models provided different results at the two stations: the OPELM worked well at Ain Dalia while OSELM was more accurate at Zit Emba. More importantly, the inclusion of the periodicity did not lead to a significant improvement in the accuracy of the models. OSELM validation results, with a coefficient of correlation R=0.872, a root mean square error RMSE =1.698mm, and a mean absolute error MAE=1.311mm outperformed OPELM (R=0.853, RMSE=1.813mm and MAE=1.403mm) at Zit Emba. In addition, at Ain Dalia, the results indicate that OPELM model provided slightly higher prediction accuracy compared to the OSELM model (R=0.808 against 0.800; RMSE=1.447mm against 1.471mm; MAE=1.091mm against 1.084mm). This work demonstrates the ability of the OPELM and OSELM approaches for estimating daily E-pan using easily measured climatic variables.