The second stage entails a VMD approach, used to decompose the yet-unresolved high frequencies (i.e., IMF 1) into their variational modes, further discerning and establishing the data attributes to be incorporated into the ELM model to simulate the respective IMFs, Res and VM data series, aggregated as an integrative tool for multiscale runoff prediction. In the first stage of the presented model design, notable frequencies in the predictor-target data series are uncovered, utilizing the CEEMDAN algorithm where the model’s inputs are decomposed into their respective Intrinsic Mode Functions (IMFs) and the Residual (Res) series. The model utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) coupled with the variational mode decomposition (VMD) algorithms for better frequency resolution of the input datasets and the extreme learning machine (ELM) algorithm as the objective predictive model. This paper advocates a data-driven approach used to design two-phase hybrid model (i.e., CVEE-ELM). ![]() Expert systems adopted in real-time multi-scale runoff prediction are useful decision-making tools for hydrologists but the stochastic nature of any hydrological variable can pose significant challenges in attaining a reliable predictive model.
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