Hydropower generation is influenced by various factors such as precipitation, temperature, and installed capacity, with hydrometeorological factors exhibiting significant temporal variability. This study proposes a hydropower generation forecasting method based on Transformer and SE-Attention for different provinces. In the model, the outputs of the Transformer and SE-Attention modules are fed into an LSTM layer to capture long-term data dependencies. The SE-Attention module is reintroduced to enhance the model’s focus on important temporal features, and a linear layer maps the hidden state of the last time step to the final output. The proposed Transformer-LSTM-SE model was tested using provincial hydropower generation data from Yunnan, Sichuan, and Chongqing. The experimental results demonstrate that this model achieves high accuracy and stability in medium- and long-term hydropower forecasting at the provincial level, with an average accuracy improvement of 33.79% over the LSTM model and 24.30% over the Transformer-LSTM model.

With global warming and intensified human activities, extreme convective precipitation has become one of the most frequent natural disasters. An accurate and reliable assessment of severe convective precipitation events can support social stability and economic development. In order to investigate the accuracy enhancement methods and data fusion strategies for the assessment of severe convective precipitation events, this study is driven by the horizontal reflectance factor (Z_H) and differential reflectance (Z_DR) of the dual-polarization radar. This research work utilizes microphysical information of convective storms provided by radar variables to construct the precipitation event assessment model. Considering the problems of high dimensionality of variable data and low computational efficiency, this study proposes a dual-polarization radar echo-data-layering strategy. Combined with the results of mutual information (MI), this study constructs Bayes–Kalman filter (KF) models (RF, SVR, GRU, LSTM) for the assessment of severe convective precipitation events. Finally, this study comparatively analyzes the evaluation effectiveness and computational efficiency of different models. The results show that the data-layering strategy is able to reduce the data dimensions of 256 × 256 × 34,978 to 5 × 2213, which greatly improves the computational efficiency. In addition, the correlation coefficient of interval III–V calibration period is increased to 0.9, and the overall assessment accuracy of the model is good. Among them, the Bayes–KF-LSTM model has the best assessment effect, and the Bayes–KF-RF has the highest computational efficiency. Further, five typical precipitation events are selected for validation in this study. The stratified precipitation dataset agrees well with the near-surface precipitation, and the model’s assessment values are close to the observed values. This study completely utilizes the microphysical information offered by dual-polarized radar Z_H and Z_DR in precipitation event assessment, which provides a wide range of application possibilities for the assessment of severe convective precipitation events.