Innovative daily runoff prediction model integrating black-winged kite algorithm and Mamba2–Transformer architecture

作者：Dongmei Xu, Xiao-xue Hu, Wenchuan Wang, Jun Wang, Can-can Shi, Hong-fei Zang · 发表于：Ecological Informatics · 年份：2025 · DOI：10.1016/j.ecoinf.2025.103565 · 被引用次数：4 · 研究领域：Hydrological Forecasting Using AI、Flood Risk Assessment and Management、Precipitation Measurement and Analysis

The accurate prediction of daily runoff is crucial for effective water resource management, flood prevention, and disaster mitigation. However, current runoff prediction models face dual challenges in extracting discriminative features from random and non-stationary data, as well as enhancing prediction performance. To overcome these challenges and improve prediction accuracy, this study proposes a hybrid feature optimization and variational prediction model (HFOVPM). The HFOVPM combines an optimized feature extraction framework with advanced techniques of temporal pattern recognition. First, the Black-winged Kite Algorithm was employed to optimize variational mode decomposition parameters for effective signal decomposition. To enhance feature representation, the resultant components were systematically organized into multivariate input vectors. Secondly, the Mamba2 architecture was used to model nonlinear dynamical interactions within the multivariate inputs. Subsequently, its learned representations were then integrated into Transformer layers to establish enhanced global temporal dependencies. Through error back-propagation optimization, the framework improved the accuracy of predictions. The model was validated using daily runoff data from three hydrological stations representing distinct eco-hydrological regimes: an alpine snowmelt-dominated basin, a subtropical rainfall-driven basin, and a mixed rain-snow basin. The key findings were as follows: (1) The HFOVPM consisten...