The trend of global Sea Surface Temperature (SST) has attracted widespread attention in several ocean-related fields such as global warming, marine environmental protection and marine biodiversity. Sea surface temperature is influenced by climate change; with the accumulation of data from ocean remote sensing observations year by year, many scholars have started to use deep learning methods for SST prediction. In this paper, we use a dynamic region partitioning approach to process ocean big data and design a framework applied to a global SST short-term prediction system. On the architecture of a Long Short-Term Memory (LSTM) network, two deep learning multi-region SST prediction models are proposed, which extract temporal and spatial information of SST by encoding, using feature transformation and decoding to predict future multi-step states. The models are tested using OISST data and the model performance is evaluated by different metrics. The proposed MR-EDLSTM model and MR-EDConvLSTM model obtained the best results for short-term prediction, with RMSE ranging from 0.2712 °C to 0.6487 °C and prediction accuracies ranging from 97.60% to 98.81% for ten consecutive days of prediction. The results show that the proposed MR-EDLSTM model has better prediction performance in coastal areas, while the MR-EDConvLSTM model performs better in predicting the sea area near the equator. In addition, the proposed deep learning model has a smaller RMSE compared to the forecasting system based on the ocean model, indicating that the deep learning method has certain advantages in predicting global SST.

With the continuous deepening of ocean exploration and the growth of emergency rescue needs, the hydroacoustic transducer, as an important component of sonar, has become more and more demanding. To improve the reception performance of the piezoelectric transducer, we developed a high-frequency, high-sensitivity planar piezoelectric transducer. A variant 1-3-2 type piezoelectric material is used to improve the effective electromechanical coupling coefficient, the upper surface of the material is covered with a brass plate to amplify the reception stress, and two variant 1-3-2 type piezoelectric materials with different dimensional parameters are arranged vertically to expand the bandwidth. We investigated the material properties and determined the material dimensions actually used to fabricate a planar hydroacoustic transducer utilizing electromechanical equivalent circuit analysis methods and finite element simulations. A prototype planar hydroacoustic transducer was fabricated and tested for resonant frequency, receiver sensitivity, bandwidth, and horizontal directivity then. The results show that the transducer’s resonant frequency is about 218.7 kHz, the maximum receiving sensitivity is -178.7 dB, the 3 dB loss receiving bandwidth is about 45 kHz, and the horizontal directivity is about 17°. The test results all indicate that the developed hydroacoustic transducer has great potential for application in marine exploration and underwater emergency rescue.

A basic feature of El Niño is an abnormal increase in the surface temperature of the equatorial Pacific Ocean, which can throw ocean–atmosphere interactions out of balance, resulting in heavy rainfall and severe storms. This climate anomaly causes different levels of impacts worldwide, such as causing droughts in some regions and excessive rainfall in others. Therefore, it is important to determine the formation of El Niño by predicting the changes in the sea surface temperature (SST) in the equatorial Pacific Ocean. In this paper, we propose a multi-element fusion network model based on convolutional long short-term memory (ConvLSTM) and an attention mechanism to predict the SST and analyze the effects of different elemental inputs on the model’s prediction performance using the prediction results. The experimental results show that using the sea surface wind (SSW) and sea level anomaly (SLA) as multi-element inputs to predict the SST overcame the shortcomings of the single-element forecast model, and the prediction accuracy of the two-element fusion model was better than that of the three-element fusion model. In the two-element fusion model, using the SSW as an input predicted the SST with a lower prediction error than using the SLA as an input and had better prediction performance compared with other benchmark models. For predicting the SST in the equatorial Pacific Ocean, the monthly average root mean square error (RMSE) was mainly concentrated in the range of 0.4–0.8 °C, and the regions with a larger error dispersion were located in the spatial range of 5° S–5° N and 130° W–90° W, and the monthly average regional RMSE was mainly concentrated in the range of 0.5–1 °C. Finally, we also validated the prediction performance of the model for the SST in El Niño and La Niña years, and the prediction results of the model in La Niña years were better than those in El Niño years.

Ocean temperature has an important influence on the distribution and migration of marine fishes, and with the improvement of modern remote sensing information acquisition technology, ocean data are constantly being improved. Sea surface temperature (SST) around the Korean Peninsula is influenced by several complex factors. In this paper, an improved long short-term memory network (LSTM) model with attention mechanism is proposed to predict the SST for the next 5 days, which extracts more temporal and spatial information by assigning new weights through the attention mechanism. The experimental results show that the root mean square error (RMSE) of the proposed model on day 1 is 0.2181°C and the prediction accuracy (PACC) is 99.14%, which is a 20% reduction in RMSE compared to the existing similar networks.