Prediction of Sea Surface Temperature using Long Short-Term Memory (1705.06861v1)

Abstract: This letter adopts long short-term memory(LSTM) to predict sea surface temperature(SST), which is the first attempt, to our knowledge, to use recurrent neural network to solve the problem of SST prediction, and to make one week and one month daily prediction. We formulate the SST prediction problem as a time series regression problem. LSTM is a special kind of recurrent neural network, which introduces gate mechanism into vanilla RNN to prevent the vanished or exploding gradient problem. It has strong ability to model the temporal relationship of time series data and can handle the long-term dependency problem well. The proposed network architecture is composed of two kinds of layers: LSTM layer and full-connected dense layer. LSTM layer is utilized to model the time series relationship. Full-connected layer is utilized to map the output of LSTM layer to a final prediction. We explore the optimal setting of this architecture by experiments and report the accuracy of coastal seas of China to confirm the effectiveness of the proposed method. In addition, we also show its online updated characteristics.

• The paper introduces an LSTM-based framework that effectively models temporal dependencies and overcomes gradient issues inherent in traditional RNNs.
• The paper validates the approach using NOAA’s high-resolution SST data from the Bohai Sea, achieving reduced RMSE and increased accuracy compared to SVR.
• The paper demonstrates the model’s adaptability through online updates, offering a versatile tool for enhanced real-time oceanographic forecasting.

Prediction of Sea Surface Temperature using Long Short-Term Memory

The paper "Prediction of Sea Surface Temperature using Long Short-Term Memory" presents a novel application of Long Short-Term Memory (LSTM) networks for predicting Sea Surface Temperature (SST). SST is a key parameter in understanding Earth's energy balance and plays a critical role in climate dynamics and biological systems. Accurate SST predictions are crucial for numerous applications such as ocean weather forecasting, offshore activities, and environmental protection, particularly in coastal areas where temperature variability is more pronounced.

Methodology

This paper reformulates SST prediction as a time series regression problem, leveraging LSTM networks to model temporal relationships within the data. LSTM, a variant of recurrent neural networks (RNNs), introduces a gate mechanism that effectively addresses the vanishing and exploding gradient issues common in vanilla RNNs, thereby capturing long-term dependencies more accurately.

The proposed network framework includes an LSTM layer to encode the temporal sequences of SST data followed by a fully-connected dense layer that transforms the LSTM outputs into final predictions. The model is optimized through experiments to determine the optimal number of LSTM layers, neuron units, and time steps for input sequences. A systematic evaluation using the Bohai Sea region, characterized by its complex SST dynamics, underpins the effectiveness of this approach.

Experimental Setup and Results

The authors conducted extensive experiments using the NOAA OI SST V2 High Resolution Dataset. The data was split into training, validation, and testing sets to ensure a robust evaluation of model performance. A comparative analysis with Support Vector Regression (SVR) highlighted the superior performance of the LSTM-based approach, particularly in terms of Root Mean Squared Error (RMSE) and prediction accuracy (ACC). The results demonstrate that the LSTM network achieves lower error rates and higher accuracy across various prediction lengths, reinforcing its potential for real-time SST forecasting.

In the context of one day to one month forecasts, LSTM networks consistently outperformed SVR, marked by a notable reduction in RMSE. Furthermore, the model's capacity for online updates was examined by integrating new data into the existing model, evidencing adaptability in real-world applications where constant model refinement is necessary.

Implications and Future Directions

The findings presented in this paper hold significant implications for enhancing predictive models of oceanographic parameters. The use of LSTM networks for SST prediction offers a promising alternative to traditional methods, with potential applications extending to anomaly detection and ocean front tracking. The paper suggests that the proposed methodology is resolution-independent, providing a versatile tool for both high and low-resolution SST data.

Theoretically, this paper extends the applicability of LSTM networks beyond standard benchmarks, encouraging further exploration of deep learning models in environmental sciences. Practically, it opens avenues for integrating data-driven approaches to marine and climatic modeling, which could influence decision-making processes in maritime safety, resource management, and ecological conservation.

Future research could explore optimizing network architectures for specific oceanic conditions or integrating multi-modal data sources to enhance forecast precision. Moreover, examining the interplay of LSTM networks with other machine learning frameworks may offer additional insights and improvements in the predictive capacity of these systems.

In conclusion, this paper contributes substantively to the field of SST prediction by establishing the efficacy of LSTM networks in capturing complex temporal dependencies inherent in climatic data, with its novel application potentially inspiring future innovations in environmental modeling using artificial intelligence.