Sea surface temperature (SST) is an important physical factor in the interaction between the ocean and the atmosphere. Accurate monitoring and prediction of the temporal and spatial distribution of SST are of great significance in dealing with climate change, disaster prevention, disaster reduction, and marine ecological protection. This study establishes a prediction model of sea surface temperature for the next five days in the East China Sea using long-term and short-term memory neural networks (LSTM). It investigates the influence of different parameters on prediction accuracy. The sensitivity experiment results show that, based on the same training data, the length of the input data of the LSTM model can improve the model’s prediction performance to a certain extent. However, no obvious positive correlation is observed between the increase in the input data length and the improvement of the model’s prediction accuracy. On the contrary, the LSTM model’s performance decreases with the prediction length increase. Furthermore, the single-point prediction results of the LSTM model for the estuary of the Yangtze River, Kuroshio, and the Pacific Ocean are accurate. In particular, the prediction results of the point in the Pacific Ocean are the most accurate at the selected four points, with an RMSE of 0.0698 °C and an R2 of 99.95%. At the same time, the model in the Pacific region is migrated to the East China Sea. The model was found to have good mobility and can well represent the long-term and seasonal trends of SST in the East China Sea. 

/pdf/prediction-of-sea-surface-temperature-in-the-east-china-sea-2ilv1pcd.pdf

Prediction of Sea Surface Temperature in the East China Sea Based on LSTM Neural Network

The sea surface temperature (SST) is an environmental indicator closely related to climate, weather, and atmospheric events worldwide. Its forecasting is essential for supporting the decision of governments and environmental organizations. Literature has shown that single machine learning (ML) models are generally more accurate than traditional statistical models for SST time series modeling. However, the parameters tuning of these ML models is a challenging task, mainly when complex phenomena, such as SST forecasting, are addressed. Issues related to misspecification, overfitting, or underfitting of the ML models can lead to underperforming forecasts. This work proposes using hybrid systems (HS) that combine (ML) models using residual forecasting as an alternative to enhance the performance of SST forecasting. In this context, two types of combinations are evaluated using two ML models: support vector regression (SVR) and long short-term memory (LSTM). The experimental evaluation was performed on three datasets from different regions of the Atlantic Ocean using three well-known measures: mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The best HS based on SVR improved the MSE value for each analyzed series by [Formula: see text], [Formula: see text], and [Formula: see text] compared to its respective single model. The HS employing the LSTM improved [Formula: see text], [Formula: see text], and [Formula: see text] concerning the single LSTM model. Compared to literature approaches, at least one version of HS attained higher accuracy than statistical and ML models in all study cases. In particular, the nonlinear combination of the ML models obtained the best performance among the proposed HS versions. 

/pdf/hybrid-systems-using-residual-modeling-for-sea-surface-5r3xytcj.pdf

Hybrid systems using residual modeling for sea surface temperature forecasting

Predicting the air temperature based on spatially accurate simulations is helpful to agricultural production, commercial activities, air transportation, water transportation, power supply, and national defense. Traditional prediction is not generally based on the time series obtained from multiple meteorological stations and from a spatial perspective. In this study, a deep convolution neural network with long short-term memory (CNN-LSTM) is constructed to extract the spatiotemporal features of temperature and the correlation between meteorological elements. The accuracies of the simulated and the predicted temperatures are spatially visualized by using the Kriging interpolation method. The accuracy of air temperature obtained from the CNN-LSTM was compared with those obtained from the CNN and the LSTM to verify its performance. The results show that there were prominent spatial variations in the temperature, with a latitudinal zonal structure in the southern Ningxia and a radial zonal structure in the northern Ningxia, China. The accuracies of the simulated and predicted temperature were high in areas with a small range of the annual temperature. The accuracies of the mean monthly temperature simulated by the CNN-LSTM in spring and autumn were higher than that in the summer. The predicted annual average temperature increased each year from 2020 to 2025. The CNN-LSTM had a higher accuracy of simulating and predicting the temperature as well as a better generalization ability than the CNN and LSTM. 

Spatial Simulation and Prediction of Air Temperature Based on CNN-LSTM

Systematic Literature Review of Various Neural Network Techniques for Sea Surface Temperature Prediction Using Remote Sensing Data

/pdf/hybrid-systems-using-residual-modeling-for-sea-surface-1qrym4yj.pdf

Time-Series Graph Network for Sea Surface Temperature Prediction

Structure-adaptive graph neural network with temporal representation and residual connections

With the increasing variety and quantity of medical devices, there is an urgent need for safe and efficient management of them. The traditional centralized management system has the problems of information asymmetry and low security. Blockchain technology can effectively guarantee the immutability and reliability of system information. However, the management of medical devices requires the participation of multiple parties and the process is complicated. The existing methods still can not simply realize the whole-process tracking management of medical devices. To address this problem, we design and implement a medical device management system using smart contract on blockchain. First, we design a complete medical device management process. Then, we design smart contracts to implement bidding purchase, contract communication, inventory management, and multi-department joint supervision of medical devices. Finally, we test the transaction processing performance of the system. The results show that our system is effective and feasible.

A Medical Device Management System Using Smart Contract on Blockchain

Bridging Trajectory-Aware Evolutionary Graph Learning and Large Language Models for Enhancing Navigability in Social Internet of Things

Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion

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