Multivariate runoff prediction by employing decomposition techniques, sample entropy, and sequence2sequence framework utilizing spatio-temporal attention

Abstract The exploitation of hydropower provides cleaner, sustainable and cheaper energy contrary to fossil fuels. Therefore, hydropower offers prospects to meet the sustainable development goals of the United Nations. These benefits motivate this study to develop different models for efficient runoff prediction utilizing multivariate hydro-meteorological data. The techniques employed for this purpose include correlation analysis, time series decomposition, sample entropy (SE), and sequence2sequence (S2S) algorithm with spatio-temporal attention (STAtt). The decomposition techniques include improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN) and the maxim overlap discrete wavelet transform (MODWT). The ICEEMDAN-STAtt-S2S model reveals the best prediction results over the counterpart hybrid and standalone models in terms of statistical metrics and comparison plots. The surpassed prediction outcomes substantiate the merger of ICEEMDAN and S2S utilizing STAtt for runoff prediction. Moreover, ICEEMDAN-STAtt-S2S offers the potential for reliable prediction of similar applications, including renewable energy, environment monitoring, and energy resources management.