Sea surface temperature prediction model based on long and short-term memory neural network

The sea surface temperature (SST) is an important parameter of the energy balance on the Earth’s surface. SST prediction is crucial to marine production, marine protection, and climate prediction. However, the current SST prediction model still has low precision and poor stability. In this study, a medium and long-term SST prediction model is designed on the basis of the gated recurrent unit (GRU) neural network algorithm. This model captures the SST time regularity by using the GRU layer and outputs the predicted results through the fully connected layer. The Bohai Sea, which is characterized by a large annual temperature difference, is selected as the study area, and the SSTs on different time scales (monthly and quarterly) are used to verify the practicability and stability of the model. The results show that the designed SST prediction model can efficiently fit the results of the real sea surface temperature, and the correlation coefficient is above 0.98. Regardless of whether monthly or quarterly data are used, the proposed network model performs better than long short-term memory in terms of stability and accuracy when the length of the prediction increases. The root mean square error and mean absolute error of the predicted SST are mostly within 0–2.5 °C.

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Temperature Prediction Based on Bidirectional Long Short-Term Memory and Convolutional Neural Network Combining Observed and Numerical Forecast Data

Sensors ◽

10.3390/s21030941 ◽

2021 ◽

Vol 21 (3) ◽

pp. 941

Author(s):

Seongyoep Jeong ◽

Inyoung Park ◽

Hyun Soo Kim ◽

Chul Han Song ◽

Hong Kook Kim

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Image Data ◽

Short Term ◽

Temperature Prediction ◽

Term Memory ◽

Long Short Term Memory

Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network-based temperature prediction model time-series weather data obtained from an automatic weather station and image data from a regional data assimilation and prediction system (RDAPS). To accommodate such different types of data into a single model, a bidirectional long short-term memory (BLSTM) model and a convolutional neural network (CNN) model are chosen to represent the features from the time-series observed data and the RDAPS image data. The two types of features are combined to produce temperature predictions for up to 14 days in the future. The performance of the proposed temperature prediction model is evaluated by objective measures, including the root mean squared error and mean bias error. The experiments demonstrated that the proposed model combining both the observed and RDAPS image data is better in all performance measures for all prediction periods compared with the BLSTM-based model using observed data and the CNN-BLSTM-based model using RDAPS image data alone.

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Temperature Prediction Using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network

Atmosphere ◽

10.3390/atmos10110718 ◽

2019 ◽

Vol 10 (11) ◽

pp. 718 ◽

Cited By ~ 2

Author(s):

Park ◽

Kim ◽

Lee ◽

Kim ◽

Song ◽

...

Keyword(s):

Neural Network ◽

Missing Data ◽

Prediction Model ◽

Short Term Memory ◽

Weather Data ◽

Short Term ◽

Temperature Prediction ◽

Term Memory ◽

Proposed Model ◽

Long Short Term Memory

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

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Prediction of Sea Surface Temperature Using Long Short-Term Memory

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2017.2733548 ◽

2017 ◽

Vol 14 (10) ◽

pp. 1745-1749 ◽

Cited By ~ 80

Author(s):

Qin Zhang ◽

Hui Wang ◽

Junyu Dong ◽

Guoqiang Zhong ◽

Xin Sun

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Short Term Memory ◽

Sea Surface ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Available capacity computation model based on long short-term memory recurrent neural network for gelled-electrolyte batteries in golf carts

IEEE Access ◽

10.1109/access.2021.3123789 ◽

2021 ◽

pp. 1-1

Author(s):

Ching-Ming Lai ◽

Ting-Jung Kuo

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Computation Model ◽

Short Term ◽

Term Memory ◽

Model Based ◽

Long Short Term Memory ◽

Golf Carts

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Long Short-term Memory Network Prediction Model Based on Fuzzy Time Series

2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS) ◽

10.1109/icaiis49377.2020.9194902 ◽

2020 ◽

Author(s):

Hua Qu ◽

Jiaqi Li ◽

Yanpeng Zhang

Keyword(s):

Time Series ◽

Prediction Model ◽

Short Term Memory ◽

Fuzzy Time Series ◽

Short Term ◽

Term Memory ◽

Model Based ◽

Memory Network ◽

Network Prediction ◽

Long Short Term Memory

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A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network

Wireless Communications and Mobile Computing ◽

10.1155/2021/6210627 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yangzi Zhao

Keyword(s):

Neural Network ◽

Stock Market ◽

Deep Neural Network ◽

Short Term Memory ◽

Stock Index ◽

Prediction Algorithm ◽

Short Term ◽

Term Memory ◽

Model Based

The stock market is affected by economic market, policy, and other factors, and its internal change law is extremely complex. With the rapid development of the stock market and the expansion of the scale of investors, the stock market has produced a large number of transaction data, which makes it more difficult to obtain valuable information. Because deep neural network is good at dealing with the prediction problems with large amount of data and complex nonlinear mapping relationship, this paper proposes an attention-guided deep neural network stock prediction algorithm. This paper synthesizes the daily stock social media text emotion index and stock technology index as the data source and applies them to the long-term and short-term memory neural network (LSTM) model to predict the stock market. The stock emotion index is extracted by constructing a social text classification emotion model of bidirectional long-term and short-term memory neural network (Bi-LSTM) based on attention mechanism and glove word vector representation algorithm. In addition, a dimensionality reduction model based on decision tree (DT) and principal component analysis (PCA) is constructed to reduce the dimensionality of stock technical indicators and extract the main data information. Furthermore, this paper proposes a model based on nasNet for pattern recognition. The recognition results can be used to automatically identify short-term K-line patterns, predict reliable trading signals, and help investors customize short-term high-efficiency investment strategies. The experimental results show that the prediction accuracy of the proposed algorithm can reach 98.6%, which has high application value.

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