In this modern era, attention has been devoted to blind super-resolution design, which improves image restoration performance by combining self-attention networks and explicitly introducing degradation information. This paper proposes a novel model called Kernel Adaptive Swin Transformer (KAST) to address the ill-posedness in image super-resolution and the resulting irregular difficulties in restoration, including asymmetrical degradation problems. KAST introduces four key innovations: (1) local degradation-aware modeling, (2) parallel attention-based feature fusion, (3) log-space continuous position bias, and (4) comprehensive validation on diverse datasets. The model captures degraded information in different regions of low-resolution images, effectively encodes and distinguishes these degraded features using self-attention mechanisms, and accurately restores image details. The proposed approach innovatively integrates degraded features with image features through a parallel attention fusion strategy, enhancing the network’s ability to capture pixel relationships and achieving denoising, deblurring, and high-resolution image reconstruction. Experimental results demonstrate that our model performs well on multiple datasets, effectively verifying the effectiveness of the proposed method.

Despite the promise of graph neural networks (GNNs) in hydrological forecasting, existing approaches face critical limitations in capturing dynamic spatiotemporal correlations and integrating physical interpretability. To bridge this gap, we propose a spatial-temporal graph neural network (ST-GNN) that addresses these challenges through three key innovations: dynamic graph construction for adaptive spatial correlation learning, a physically-informed feature enhancement layer for soil moisture and evaporation integration, and a hybrid Graph-LSTM module for synergistic spatiotemporal dependency modeling. The temporal and spatial modules of the spatio-temporal graph neural network exhibit a structural symmetry, which enhances the model’s representational capability. By integrating these components, the model effectively represents rainfall-runoff processes. Experimental results across four Chinese watersheds demonstrate ST-GNN’s superior performance, particularly in semi-arid regions where prediction accuracy shows significant improvement. Compared to the best-performing baseline model (ST-GCN), our ST-GNN achieved an average reduction in root mean square error (RMSE) of 6.5% and an average improvement in the coefficient of determination (R²) of 1.8% across 1–8 h forecast lead times. Notably, in the semi-arid Pingyao watershed, the improvements reached 13.3% in RMSE reduction and 2.5% in R² enhancement. The model incorporates watershed physical characteristics through a feature fusion layer while employing an adaptive mechanism to capture spatiotemporal dependencies, enabling robust watershed-scale forecasting across diverse hydrological conditions.