Strong tropical cyclones have made a drastic effect on human life and natural environment. As large amounts of meteorological data and monitoring data continue to accumulate, traditional methods for predicting tropical cyclone tracks face numerous challenges regarding their prediction efficiency and accuracy. Deep learning methods recently have been proven to be able to learn both spatial and temporal features from a large amount of dataset and be extremely efficient and accurate for forecasting data in complex structures. In this paper, we propose a novel data-driven deep learning model to predict tropical cyclone tracks using the spatial locations and multiple meteorological factors. This model comprises a multidimensional feature selection layer, a CNN layer and a GRU layer. The proposed model was trained using a dataset of real-world tropical cyclones from the years 1945 to 2017. Through the comparison experiments, the results verify that the proposed model outperforms the traditional forecasting methods, including a climatologically aware forecasting technique, the Sanders Barotropic technique and a numerical weather prediction (NWP) model. In addition, the proposed model has better accuracy than some deep learning methods, including RNN, GRU, CNN, AE-RNN, CNN-RNN, and CNN-GRU without the proposed feature selection layer.

Aspect-based sentiment analysis is a fine-grained task where the key goal is to predict sentiment polarities of one or more aspects in a given sentence. Currently, graph neural network models built upon dependency trees are widely employed for aspect-based sentiment analysis tasks. However, most existing models still contain a large amount of noisy nodes that cannot precisely capture the contextual relationships between specific aspects. Meanwhile, most studies do not consider the connections between nodes without direct dependency edges but play critical roles in determining the sentiment polarity of an aspect. To address the aforementioned limitations, we propose a Structured Dependency Tree-based Graph Convolutional Network (SDTGCN) model. Specifically, we explore construction of a structured syntactic dependency graph by incorporating positional information, sentiment commonsense knowledge, part-of-speech tags, syntactic dependency distances, etc., to assign arbitrary edge weights between nodes. This enhances the connections between aspect nodes and pivotal words while weakening irrelevant node links, enabling the model to sufficiently express sentiment dependencies between specific aspects and contextual information. We utilize part-of-speech tags and dependency distances to discover relationships between pivotal nodes without direct dependencies. Finally, we aggregate node information by fully considering their importance to obtain precise aspect representations. Experimental results on five publicly available datasets demonstrate the superiority of our proposed model over state-of-the-art approaches; furthermore, the accuracy and F1-score show a significant improvement on the majority of datasets, with increases of 0.74, 0.37, 0.65, and 0.79, 0.75, 1.17, respectively. This series of enhancements highlights the effective progress made by the STDGCN model in enhancing sentiment classification performance.

Under global climate change, the frequency of typhoons and their strong wind, heavy rain, and storm surge increase, seriously threatening the life and property of human society. However, traditional tropical cyclone track prediction methods have difficulties in processing large amounts of complex data in terms of prediction efficiency and accuracy. Recently, deep learning methods have shown a potential capability to process complex data efficiently and accurately. In this paper, we propose a novel data-driven approach based on auto-encoder (AE) and gated recurrent unit (GRU) models to forecast tropical cyclone landing locations using the historical tropical cyclone tracks and various meteorological attributes. This approach fuses a data preprocessing layer, an AE layer, and a GRU layer with a customized batch process. The model is trained on a real-world tropical cyclone dataset from the years 1945–2017. Through a comparison with existing forecasting methods, the results verified that our proposed model performed around 15%, 42%, and 56% better than the Numerical Weather Prediction model (NWP) in 24, 48, and 72 h forecasts, and 27%, 13%, 17%, and 17% better than RNN, AE-RNN, GRU, and LSTM, respectively, in 24 h forecasts, using the absolute position error. In addition, a comparison of the meteorological variables indicated that the variable maximum sustained wind speed had the most significant effect on tropical cyclone track prediction.

Opinion-leader mining in social networks is a critical problem in research of the information dissemination process and in public opinion guidance and supervision. Not every social network user has a high probability to be an opinion leader. However, most mining methods identify opinion leaders among users in the whole network, which adds unnecessary calculations. To solve this problem, we propose a rank after clustering (RaC) algorithm to mine opinion leaders in social networks with a phased-clustering perspective, which has the following aspects: (1) Aiming to reduce the scale of calculation, the clustering stage clusters users in social networks using a K-means algorithm according to topological information to find the set of opinion leader candidates; (2) The ranking stage determines the user ranks of opinion leader candidates by both their activeness and influence, and we accumulate the followers' influence weighted by degree of attention when assessing user influence. In experiments, a new indicator, the C-value, and simulations based on the linear threshold model are used to evaluate the performance of the RaC algorithm. The results show that RaC is effective and accurate.

Recently, with the accumulation of remote sensing data, the traditional tropical cyclone (TC) track prediction methods (e.g., dynamic methods and statistical methods) have limitations in prediction efficiency and accuracy when dealing with a large amount of data. However, deep learning methods begin to show their advantages to capture the complex spatiotemporal features in high-dimensional data. The task of TC track prediction based on remote sensing images can be formulated as a spatiotemporal sequence-to-sequence problem. Therefore, a novel encoding-to-forecasting model with convolutional long short-term memory (ConvLSTM) and spatial attention network (SAN-EFSModel) was proposed to predict TC tracks in this paper, which can fully extract the long-term spatial and temporal features. The proposed model was evaluated on the real remote sensing images in the western North Pacific Ocean to forecast 24-h TC tracks. Compared with ECMWF-HRES model and NCEP-GFS model, the proposed method has a better prediction accuracy in the testing set with an average position error about 30 km less. Compared with the deep learning methods, the proposed method also has the best performance. Significance Statement: Tropical cyclones have a great impact on human life and natural environment due to their high frequency of occurrence, heavy degree of harm, wide impact range, and long disaster chain. In this article, we propose an encoding-to-forecasting model with convolutional long short-term memory (ConvLSTM) and spatial attention network (SAN-EFSModel) to predict tropical cyclone tracks, in which the SAN module and the convolution module of ConvLSTM have the ability to extract spatial features, and the long short-term memory (LSTM) module of ConvLSTM can extract temporal features from remote sensing images and tropical cyclone tracks. The results show that the average position error (APE) of our proposed method has about 30% improvement in a 24-h forecast compared with prevailing numerical weather prediction models.

Wind downscaling is crucial for refining coarse‐scale wind estimates, improving local‐scale predictions, and supporting various applications like risk assessment and planning. Dynamic downscaling models demand extensive computational resources and time, leading to a shift toward more efficient statistical downscaling, whereas it often overlooks inter‐variable and inter‐station spatial correlations. Addressing this, we propose TerraWind, a deep learning‐based downscaling method for complex terrain regions. TerraWind enhances accuracy by incorporating topographic factors and inter‐station linkages, capturing wind field interactions with terrain at multiple scales. Experimental results in Eastern China demonstrate that TerraWind reduces wind speed Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by an average of 42.6 and 33.3, respectively, compared to three interpolation methods (bicubic, bilinear, and Inverse Distance Weighting). Furthermore, TerraWind achieves an average reduction of 35.3 in wind speed MAE and 25.6 in wind speed RMSE compared to four deep learning models (Wind‐Topo, DeepCAMS, RCM‐emulator, and Uformer).