Short-term precipitation forecasting methods are mainly divided into statistical forecasting, numerical model-based forecasting, and radar image extrapolation techniques. The two methods based on statistical prediction and numerical model have the disadvantages of being unstable and generating large errors. Therefore, this study proposes the use of deep learning for radar image extrapolation for precipitation forecasting, in particular by developing algorithms for ConvLSTM and SmaAT-UNet. The ConvLSTM model is a fusion of a CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory network), which solves the challenge of processing spatial sequence data, which is a task that traditional LSTM models cannot accomplish. At the same time, SmaAT-UNet enhances the traditional UNet structure by incorporating the CBAM (Convolutional Block Attention Module) attention mechanism and replacing the standard convolutional layer with depthwise separable convolution. This innovative approach aims to improve the efficiency and accuracy of short-term precipitation forecasting by improving feature extraction and data processing techniques. Evaluation and analysis of experimental data show that both models exhibit good predictive ability, with the SmaAT-UNet model outperforming ConvLSTM in terms of accuracy. The results show that the performance indicators of precipitation prediction, especially detection probability (POD) and the Critical Success index (CSI), show a downward trend with the extension of the prediction time. This trend highlights the inherent challenges of maintaining predictive accuracy over longer periods of time and highlights the superior performance and resilience of the SmaAT-UNet model under these conditions. Compared with the statistical forecasting method and numerical model forecasting method, its accuracy in short-term rainfall forecasting is improved.

Timely and accurate rainfall prediction is crucial to social life and economic activities. Because of the influence of numerous factors on rainfall, making precise predictions is challenging. In this study, the northern Xinjiang region of China is selected as the research area. Based on the pattern of rainfall in the local area and the needs of real life, rainfall is divided into four levels, namely ‘no rain’, ‘light rain’, ‘moderate rain’, and ‘heavy rain and above’, for rainfall levels nowcasting. To solve the problem that the existing model can only extract a single time dependence and cause the loss of some valuable information in rainfall data, a prediction model named DFFNet, which is based on dual-branch feature fusion, is proposed in this paper. The two branches of the model are composed of Transformer and CNN, which are used to extract time dependence and feature interaction in meteorological data, respectively. The features extracted from the two branches are fused for prediction. To verify the performance of DFFNet, the India public rainfall dataset and some sub-datasets in the UEA dataset are chosen for comparison. Compared with the baseline models, DFFNet achieves the best prediction performance on all the selected datasets; compared with the single-branch model, the training time consumption of DFFNet on the two rainfall datasets is reduced by 21% and 9.6%, respectively, and it has a faster convergence speed. The experimental results show that it has certain theoretical value and application value for the study of rainfall nowcasting.

Joint entity and relation extraction methods have attracted an increasing amount of attention recently due to their capacity to extract relational triples from intricate texts. However, most of the existing methods ignore the association and difference between the Named Entity Recognition (NER) subtask features and the Relation Extraction (RE) subtask features, which leads to an imbalance in the interaction between these two subtasks. To solve the above problems, we propose a new joint entity and relation extraction method, FSN. It contains a Filter Separator Network (FSN) module that employs a two-direction LSTM to filter and separate the information contained in a sentence and merges similar features through a splicing operation, thus solving the problem of the interaction imbalance between subtasks. In order to better extract the local feature information for each subtask, we designed a Named Entity Recognition Generation (NERG) module and a Relation Extraction Generation (REG) module by adopting the design idea of the decoder in Transformer and average pooling operations to better capture the entity boundary information in the sentence and the entity pair boundary information for each relation in the relational triple, respectively. Additionally, we propose a dynamic loss function that dynamically adjusts the learning weights of each subtask in each epoch according to the proportionality between each subtask, thus narrowing down the difference between the ideal and realistic results. We thoroughly evaluated our model on the SciERC dataset and the ACE2005 dataset. The experimental results demonstrate that our model achieves satisfactory results compared to the baseline model.

Transfer learning plays a crucial role in low-resource machine translation by addressing the challenge of poor model performance due to limited data in low-resource languages, thereby improving translation accuracy. Current research methods not only utilize pre-trained parent models for parameter initialization and fine-tuning but also use the soft labels output by these parent models to enhance the consistency between parent and child models. However, even if the parent model performs well, there are still instances where certain token predictions are unstable. During training, if the child model incorporates these unstable token predictions, it can hinder its learning effectiveness; the child model might not fully comprehend the parent model’s prediction strategy, potentially affecting overall translation performance. To address this, we propose a training strategy called Token-Level Filter Training, designed to effectively filter out unstable token predictions from the parent model, thereby transferring the parent model’s positive knowledge to the child model. Additionally, we introduce a hierarchical ranking loss method to help the child model better learn the parent model’s prediction strategies and sequence order, thus enhancing translation accuracy and fluency. Experimental results show that our method outperforms baseline methods on the public datasets Global Voices (Id, Ca, Hu, Pl) and WMT17 (Turkish–English), with BLEU score improvements of 1.47, 0.91, 0.50, 0.54, and 0.55, respectively. These results demonstrate the effectiveness and superiority of the proposed method.

In the field of weather forecasting, short-term precipitation forecast is still a global problem. The complicated mechanism of convective weather system, the medium and small scale weather system brings great difficulties to the forecast, which is difficult to detect, understand and forecast. The application of short-term precipitation forecast is still in the initial stage. At present, the optical flow method based on radar echo is widely used in short-term and imminent precipitation prediction, but the optical flow method has obvious limitations. In order to improve the forecast precision of short-term and imminent precipitation forecast model, this study introduces the method of depth learning to build the forecast model. The application of depth learning to short-term precipitation forecasting is still in its initial stage. We analyze the existing models may exist in the shortcomings of the proposed in-depth learning model combination of different ideas. By combining different network structures (such as CONVLSTM, CONVGRU, etc.), we can get a better combined forecasting model. It has certain application value.