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

Abstract

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 (Epan). 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 (Tmax, Tmin), wind speed (U2), relative humidity (RH %) and Epan data were used as inputs to the models. Pan evaporation Epan 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\u2009=\u20090.872, a root mean square error RMSE =1.698\xa0mm, and a mean absolute error MAE\u2009=\u20091.311\xa0mm outperformed OPELM (R\u2009=\u20090.853, RMSE\u2009=\u20091.813\xa0mm and MAE\u2009=\u20091.403\xa0mm) 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\u2009=\u20090.808 against 0.800; RMSE\u2009=\u20091.447\xa0mm against 1.471\xa0mm; MAE\u2009=\u20091.091\xa0mm against 1.084\xa0mm). This work demonstrates the ability of the OPELM and OSELM approaches for estimating daily Epan using easily measured climatic variables.