To increase the accuracy of drought prediction, this study proposes a drought forecasting method based on the Informer model. Taking the Yellow River Basin as an example, the forecasting accuracies of the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Informer models on multiple timescales of the Standardized Precipitation Evapotranspiration Index (SPEI) were compared and analyzed. The results indicate that, with an increasing timescale, the forecasting accuracies of the ARIMA, LSTM, and Informer models improved gradually, reaching the best accuracy on the 24-month timescale. However, the predicted values of ARIMA, as well as those of LSTM, were significantly different from the true SPEI values on the 1-month timescale. The Informer model was more accurate than the ARIMA and LSTM models on all timescales, indicating that Informer can widely capture the information of the input series over time and is more effective in long-term prediction problems. Furthermore, Informer can significantly enhance the precision of SPEI prediction. The predicted values of the Informer model were closer to the true SPEI values, and the forecasted SPEI trends complied with the actual trends. The Informer model can model different timescales adaptively and, therefore, better capture relevance on different timecales. The NSE values of the Informer model for the four meteorological stations on SPEI24 were 0.968, 0.974, 0.972, and 0.986.

Global warming and altered precipitation patterns pose a serious threat to crop production in the North China Plain (NCP). Quantifying the frequency of adverse climate events (e.g., frost, heat and drought) under future climates and assessing how those climatic extreme events would affect yield are important to effectively inform and make science-based adaptation options for agriculture in a changing climate. In this study, we evaluated the effects of heat and frost stress during sensitive phenological stages at four representative sites in the NCP using the APSIM-wheat model. climate data included historical and future climates, the latter being informed by projections from 22 Global Climate Models (GCMs) in the Coupled Model Inter-comparison Project phase 6 (CMIP6) for the period 2031–2060 (2050s). Our results show that current projections of future wheat yield potential in the North China Plain may be overestimated; after more accurately accounting for the effects of frost and heat stress in the model, yield projections for 2031-60 decreased from 31% to 9%. Clustering of common drought-stress seasonal patterns into key groups revealed that moderate drought stress environments are likely to be alleviated in the future, although the frequency of severe drought-stress environments would remain similar (25%) to that occurring under the current climate. We highlight the importance of mechanistically accounting for temperature stress on crop physiology, enabling more robust projections of crop yields under future the burgeoning climate crisis.

Using a dataset of 114 meteorological stations in the Yangtze River Basin from 1980–2019, the standardized precipitation evapotranspiration index (SPEI) was calculated based on the Penman-Monteith evapotranspiration model for multiple time scales, and the spatial and temporal evolution characteristics and driving factors of drought in the Yangtze River Basin were analyzed by combining spatial and temporal analysis methods as well as geodetector. The main results obtained are as follows: (1) The climate of the Yangtze River Basin is an overall wet trend, and the trend of summer drought is more similar to the annual scale trend. (2) Most areas in the Yangtze River Basin showed mild drought or no drought, and there is little difference in drought condition among the Yangtze River Basin regions. The areas with drought conditions are mainly distributed in the southwest and east of the Yangtze River Basin. (3) There are significant seasonal differences in drought conditions in all regions, and the drought condition is more different in autumn compared to spring, summer and winter. (4) The average annual precipitation and elevation factors are the dominant driving factors of drought in the Yangtze River Basin, and the double-factor interaction has a greater influence on the drought variation in the Yangtze River Basin than the single-factor effect, indicating that the difference of drought condition in the Yangtze River Basin is the result of the combination of multiple factors.

The rapidly evolving field of Large Language Models (LLMs) holds immense promise for healthcare, particularly in medication guidance and adverse drug reaction prediction. Despite their potential, existing LLMs face challenges in dealing with complex polypharmacy scenarios and often grapple with data lag issues. To address these limitations, we introduce an LLM-based Chinese medication guidance system, called ShennongMGS, specifically tailored for robust medication guidance and adverse drug reaction predictions. Our system transforms multi-source heterogeneous medication information into a knowledge graph and employs a two-stage training strategy to construct a specialized LLM (ShennongGPT). This method enables the simulation of professional pharmacists’ decision-making processes and incorporates the capability for knowledge self-updating, thereby significantly enhancing drug safety and the overall quality of medical services. Rigorously evaluated by medical professionals and artificial intelligence experts, our method demonstrates superiority, outperforming existing general and specialized LLMs in performance.

As droughts become more frequent due to climate change and shifts in land use, enhancing the accuracy of drought prediction is becoming crucial for informed land and water resource management. This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. The findings indicate that the Informer model outperforms the other three models in terms of drought forecasting accuracy across all time scales. Nevertheless, the predictive capacity of the Informer model remains suboptimal when it comes to short-term intervals. Aiming at the problem of drought forecasting accuracy in a short time scale, this study proposed a drought forecasting model named VMD-JAYA-Informer based on Variational Mode Decomposition (VMD) and the JAVA optimization algorithm to improve the Informer model. This study conducted a comparative analysis of VMD-JAYA-ARIMA, VMD-JAYA-LSTM, VMD-JAYA-CNN, and VMD-JAYA-Informer drought prediction models. The performance of these models was evaluated using the root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), and Mean Absolute Error (MAE). The VMD-JAYA-Informer model’s forecast for the 1-month SPEI significantly surpasses that of alternative models and demonstrates a robust agreement with the actual data. Simultaneously, the model exhibits equally optimal forecasting performance across different time scales. In order to validate the VMD-JAYA-Informer model, four meteorological stations in the Songliao River Basin were chosen at random. The validation results demonstrate that VMD-JAYA-Informer outperforms the Informer model in terms of prediction accuracy on the 1-month time scale (NSE values of 0.8663, 0.8765, 0.8822, and 0.8416, respectively). Additionally, the model outperforms Informer in terms of prediction performance on other time scales, further demonstrating its generalizability and excellence in drought prediction on shorter time scales.

The extraction of small water bodies in the Yellow River Basin has always been a key issue of concern in the fields of remote sensing technology application, water resource management, environmental science, and geographic information systems. Due to factors such as water bodies, human activities, and cloud cover, water body extraction becomes difficult. In addition, convolutional neural networks are prone to losing small water body feature information during the process of extracting local features, which can cause more imbalance between positive and negative samples of water bodies and non-water bodies. In response to these issues, this study focused on a specific research area—the middle and lower reaches of the Yellow River. We processed and analyzed high-resolution optical satellite images collected from the Yellow River Basin and other areas, with a particular emphasis on precise identification of small water bodies, and proposed a network structure, the SE-Attention-Residual-Unet (SE-ResUnet), for water extraction tasks.The main contributions of this article are threefold: (1) Introducing a channel attention mechanism with residual structure in the down-sampling process, and learning Unet’s skipping structure for multi-scale feature extraction and compensation, thereby enhancing the feature extraction ability of small water bodies, including rivers, lakes, and reservoirs. (2) Introducing a weighted-Dice (W-Dice) loss function to balance positive and negative samples and enhance the generalization of the model. (3) In comparative experiments on improving the Unet model with semantic segmentation networks such as Unet, PSPNet, Deeplabv3+ on a self-built dataset and remote sensing interpretation public dataset, excellent performance and results were achieved on the mIoU, OA, and F1-score metrics. On the self-built dataset, compared with Unet, the mIoU, OA, and F1-score improved by 0.38%, 0.12%, and 0.08%, respectively. On the publicly available dataset, for remote sensing interpretation of water extraction, the mIoU, OA, and F1-score improved by 0.63%, 0.26%, and 0.25%, respectively. The experimental results demonstrate that a strategy combining an attention mechanism and a weighted loss function has a significant effect on the effectiveness of the collaborative improvement of neural network models in water extraction tasks.