A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data

Xiao, Changjiang; Chen, Nengcheng; Hu, Chuli; Wang, Ke; Xu, Zhiwu; Cai, Yaping; Xu, Lei; Chen, Zeqiang; Gong, Jianya

doi:10.1016/j.envsoft.2019.104502

Cited by 168 publications

(88 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kaba et al input astronomical factors, extraterrestrial radiation and climate variables, sunshine duration, cloud cover, minimum temperature, and maximum temperature as attributes to obtain a climate prediction model; the prediction accuracy of the model for the marine climate was 98% [ 17 ]. Xiao et al used convolutional Long Short-Term Memory (LSTM) networks as building blocks and trained the blocks in an end-to-end manner to achieve accurate and comprehensive predictions of sea surface temperature in the short and medium term [ 18 ]. However, there is little research on the characteristics of using satellite cloud pictures to identify typhoon levels through deep learning technology [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Zhou

Xiang

Huang

2020

Sensors

View full text Add to dashboard Cite

Typhoons are some of the most serious natural disasters, and the key to disaster prevention and mitigation is typhoon level classification. How to better use data of satellite cloud pictures to achieve accurate classification of typhoon levels has become one of classification the hot issues in current studies. A new framework of deep learning neural network, Graph Convolutional–Long Short-Term Memory Network (GC–LSTM), is proposed, which is based on the data of satellite cloud pictures of the Himawari-8 satellite in 2010–2019. The Graph Convolutional Network (GCN) is used to process the irregular spatial structure of satellite cloud pictures effectively, and the Long Short-Term Memory (LSTM) network is utilized to learn the characteristics of satellite cloud pictures over time. Moreover, to verify the effectiveness and accuracy of the model, the prediction effect and model stability are compared with other models. The results show that: the algorithm performance of this model is better than other prediction models; the prediction accuracy rate of typhoon level classification reaches 92.35%, and the prediction accuracy of typhoons and super typhoons reaches 95.12%. The model can accurately identify typhoon eye and spiral cloud belt, and the prediction results are always kept in the minimum range compared with the actual results, which proves that the GC–LSTM model has stronger stability. The model can accurately identify the levels of different typhoons according to the satellite cloud pictures. In summary, the results can provide a theoretical basis for the related research of typhoon level classification.

show abstract

Section: Introductionmentioning

confidence: 99%

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Zhou

Xiang

Huang

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…A further investigation with LSTM models was produced by Xiao et al (2019b), who compared convolutional LSTM, LSTM, and SVR to estimate the SST in the East China Sea. Their results showed that the convolutional LSTM model provided the best prediction performance among the models considered.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Haghbin

Sharafati

Motta

et al. 2021

Prog Earth Planet Sci

View full text Add to dashboard Cite

The application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.

show abstract

“…Ref. [32] forecasted sea surface temperature field by processing satellite data with the combination of CNN and LSTM and [33] predicted subsurface temperatures by using CNN with satellite remote sensing data. In this study, we performed air temperature forecasting by applying the CNN to numeric weather data rather than satellite data with a suitable structure to process them.…”

Section: Neural Network For Signal Processingmentioning

confidence: 99%

Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks

Lee

Son

2020

Applied Sciences

View full text Add to dashboard Cite

Temperature forecasting has been a consistent research topic owing to its significant effect on daily lives and various industries. However, it is an ever-challenging task because temperature is affected by various climate factors. Research on temperature forecasting has taken one of two directions: time-series analysis and machine learning algorithms. Recently, a large amount of high-frequent climate data have been well-stored and become available. In this study, we apply three types of neural networks, multilayer perceptron, recurrent, and convolutional, to daily average, minimum, and maximum temperature forecasting with higher-frequency input features than researchers used in previous studies. Applying these neural networks to the observed data from three locations with different climate characteristics, we show that prediction performance with highly frequent hourly input data is better than forecasting performance with less-frequent daily inputs. We observe that a convolutional neural network, which has been mostly employed for processing satellite images rather than numeric weather data for temperature forecasting, outperforms the other models. In addition, we combine state of the art weather forecasting techniques with the convolutional neural network and evaluate their effects on the temperature forecasting performances.

show abstract

A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data

Cited by 168 publications

References 30 publications

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks

Contact Info

Product

Resources

About