This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA).  The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff.  The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty.  And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. 
TV-DMA
The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. 
deep learning algorithm