The layout and configuration of urban infrastructure are essential for the orderly operation and healthy development of cities. With the promotion and popularization of new energy vehicles, the modeling and prediction of charging pile usage and allocation have garnered significant attention from governments and enterprises. Short-term demand forecasting for charging piles is crucial for their efficient operation. However, existing prediction models lack a discussion on the appropriate time window, resulting in limitations in station-level predictions. Recognizing the temporal nature of charging pile occupancy, this paper proposes a novel stacked-LSTM model called attention-SLSTM that integrates an attention mechanism to predict the charging demand of electric vehicles at the station level over the next few hours. To evaluate its performance, this paper compares it with several methods. The experimental results demonstrate that the attention-SLSTM model outperforms both LSTM and stacked-LSTM models. Deep learning methods generally outperform traditional time series forecasting methods. In the test set, MAE is 1.6860, RMSE is 2.5040, and MAPE is 9.7680%. Compared to the stacked-LSTM model, MAE and RMSE are reduced by 4.7%and 5%, respectively; while MAPE value decreases by 1.3%, making it superior to LSTM overall. Furthermore, subsequent experiments compare prediction performance among different charging stations, which confirms that the attention-SLSTM model exhibits excellent predictive capabilities within a six-step (2 h) window.

Analyzing drought evolution requires dynamic three-dimensional methods to capture spatiotemporal continuity. Existing approaches oversimplify drought patch connectivity by relying on overlapping logic, thereby neglecting dynamic evolution. We propose a novel three-dimensional identification method incorporating spatial autocorrelation and anisotropy. Using the ERA5 dataset and the multi-model ensemble mean (MEM) of CMIP6, we investigate meteorological drought characteristics and migration patterns in China during 1961–2010 (historical) and 2031–2080 (future, SSP2-4.5/SSP5-8.5). Results indicate future drought frequency may decline by over 70% compared to historical levels, but severity, duration, affected area, and migration distance could increase significantly. Most future droughts (96.3% for SSP2-4.5; 95.0% for SSP5-8.5) are projected in spring and summer. Drought trajectories may predominantly shift northeastward (33% for SSP2-4.5; 38% for SSP5-8.5), with migration hotspots transitioning from the upper Yangtze River Basin to the upper Yellow River Basin. These findings enhance the understanding of drought dynamics and support the development of improved drought monitoring frameworks. The methodology and projections provide critical insights for drought risk management and adaptive water resource planning under climate change.