Accurately predicting sea surface temperature (SST) is practically important to many applications, such as weather forecasting, ocean environment protection, and marine disaster prevention. The major challenge for predicting SST is to capture both the spatial and temporal characteristics of SST, which has not yet been well addressed by the existing methods. In this work, we proposed a novel dense dilated convolutional LSTM (D2CL) model to predict SST. D2CL first integrates dilated convolutional network and LSTM to learn spatial and temporal features from the SST data simultaneously. Then, it uses multiple feature extractors of different dilated kernel sizes to learn features of varying scales. Finally, it introduces dense connection to reduce feature loss during the training process and achieves SST prediction in an encoder–decoder style. We have conducted extensive experiments over two real datasets to validate the effectiveness of D2CL and all the proposed enhancing techniques. As suggested by the experimental results, D2CL outperforms the existing methods and could achieve accurate SST prediction for as long as seven days in future.

Satellite data is of high importance for ocean environment monitoring and protection. However, due to the missing values in satellite data, caused by various force majeure factors such as cloud cover, bad weather and sensor failure, the quality of satellite data is reduced greatly, which hinders the applications of satellite data in practice. Therefore, a variety of methods have been proposed to conduct missing data imputation for satellite data to improve its quality. However, these methods cannot well learn the short-term temporal dependence and dynamic spatial dependence in satellite data, resulting in bad imputation performance when the data missing rate is large. To address this issue, we propose the Spatio-Temporal Attention Generative Adversarial Network (STA-GAN) for missing value imputation in satellite data. First, we develop the Spatio-Temporal Attention (STA) mechanism based on Graph Attention Network (GAT) to learn features for capturing both short-term temporal dependence and dynamic spatial dependence in satellite data. Then, the learned features from STA are fused to enrich the spatio-temporal information for training the generator and discriminator of STA-GAN. Finally, we use the generated imputation data by the trained generator of STA-GAN to fill the missing values in satellite data. Experimental results on real datasets show that STA-GAN largely outperforms the baseline data imputation methods, especially for filling satellite data with large missing rates.

Sea surface temperature (SST) is one critical parameter of global climate change, and accurate SST prediction is important to various applications, e.g., weather forecasting, fishing directions, and disaster warnings. The global ocean system is unified and complex, and the SST patterns in different oceanic regions are highly diverse and correlated. However, existing data-driven SST prediction methods mainly consider the local patterns within a certain oceanic region, e.g., El Nino region and the Black sea. It is challenging but necessary to model the global SST correlations rather than that in a specific region to enhance the prediction accuracy of SST. In this work, we proposed a new method called Hierarchical Graph Recurrent Network (HiGRN) to address the issue. First, to learn the dynamic and diverse local SST patterns of specific locations, we design an adaptive node embedding with self-learned parameters to learn various SST patterns. Then we develop a hierarchical cluster generator to aggregate the locations with similar patterns into regional clusters and utilize a graph convolution network to learn the spatial correlations among these clusters. Finally, we introduce a multi-level attention mechanism to fuse the local patterns and regional correlations, and the output is fed into a recurrent network to achieve SST predictions. Extensive experiments on two real-world datasets show that our method largely outperforms the state-of-the-art SST prediction methods. The source code is available at https://github.com/Neoyanghc/HiGRN .

Sea surface temperature (SST) is a crucial factor that affects global climate and marine activities. Predicting SST at different temporal scales benefits various applications, from short-term SST prediction for weather forecasting to long-term SST prediction for analyzing El Niño–Southern Oscillation (ENSO). However, existing approaches for SST prediction train separate models for different temporal scales, which is inefficient and cannot take advantage of the correlations among the temperatures of different scales to improve the prediction performance. In this work, we propose a unified spatio-temporal model termed the Multi-In and Multi-Out (MIMO) model to predict SST at different scales. MIMO is an encoder–decoder model, where the encoder learns spatio-temporal features from the SST data of multiple scales, and fuses the learned features with a Cross Scale Fusion (CSF) operation. The decoder utilizes the learned features from the encoder to adaptively predict the SST of different scales. To our best knowledge, this is the first work to predict SST at different temporal scales simultaneously with a single model. According to the experimental evaluation on the Optimum Interpolation SST (OISST) dataset, MIMO achieves the state-of-the-art prediction performance.

Single-cell RNA sequencing(scRNA-seq) technology has boomed in the past decade which makes it possible to study biological problems at the resolution of cellular-level. Currently, the research mainly focuses on exploring the cellular heterogeneity, involving studies about identifying cell type identification, cell lineage tracing, spatial model reconstruction of complex organizations, etc. Clustering analysis is always the most effective way in grouping single cells in previous studies. However, existing scRNA-seq clustering methods separate pre-processing and clustering tasks that complicated the problem. In addition, the emergence of big data further limits the traditional clustering algorithms’ application on scRNA-seq data. Therefore, developing novel clustering methods and improving clustering accuracy for growing scRNA-seq data is a continuous task. In this paper, we propose a highly integrated Deep Subspace Clustering Denoise Network named DSCD, which integrates denoise, dimension reduction and clustering in a unified framework. Based on the neural network architecture of autoencoder, DSCD discovers the low dimensional latent structure within scRNA-seq data from the compressed representation. Furthermore, we add a novel self-expressive denoise layer to learning the global relationships between single cells, which is the main innovation of DSCD. Experimental results on the synthetic data demonstrate the effectiveness of the novel denoise layer. From the clustering results on 5 real scRNA-seq datasets, we find that DSCD outperforms the related subspace clustering algorithms and state of the art methods. In conclusion, DSCD responds well to the rapidly increasing scRNA-seq data scale, greatly reduces human interference in dimension reduction and handles the noisy scRNA-seq data in proper way thus obtain a higher clustering accuracy.