A deep learning runoff prediction model based on wavelet decomposition and dynamic feature fusion.

ABSTRACT: To address the stochasticity, time-varying characteristics, and nonlinear dynamics of runoff series, this research proposes a novel deep learning architecture, BWDformer, based on wavelet decomposition and dynamic feature fusion, to enhance the precision of streamflow forecasting. Based on Informer, the model innovatively integrates wavelet decomposition, a dynamic feature fusion module (DFF), and Bayesian optimization to address the limitations of conventional deep learning models in multi-scale feature integration, long-term dependency capture, and dynamic feature adjustment. Specifically, wavelet decomposition is first applied to extract multi-scale features through adaptive time windows, accurately capturing short-term fluctuations, seasonal variations, and long-term trends in runoff d

SUBMITTER: Xu DM 

PROVIDER: S-EPMC12552506 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature