Kernel extreme learning machines (KELM): a new approach for modeling monthly evaporation (EP) from dams reservoirs

Abstract

ABSTRACT In this study, four kernels extreme learning machines (KELM): radial basis function (RBELM), polynomial (POELM), wavelet (WKELM) and linear (LNELM) extreme learning machines were compared for modelling monthly pan evaporation from Algerian dams reservoirs, according to three scenarios. In the first scenario, the model were developed using splitting ratio of 70/30%, for training and validation subset, respectively, and the POELM1 achieves better performances. For the second scenario, the best models were trained using validation dataset and tested with the training dataset. Results showed that, RBELM1 would appear to yield the most accurate results, across all four dam’s reservoirs, with R2 between 0.852 and 0.949, and NSE between 0.846 and 0.946, respectively. For the third scenario, when the models were developed using pooled data and validated at each station separately, the R2 and NSE values ranged from 0.815 to 0.937 and from 0.809 to 0.928, respectively. Generally speaking the results obtained were very encouraging. Our findings show that KELM are good and more consistent models, and can predict evaporation across large climatic zones. The findings suggest that the proposed KELM is useful to help establish more robust tools and further improve available machines learning approaches.