A Convolutional Neural Network Using Surface Data to Predict Subsurface Temperatures in the Pacific Ocean

Han, Mingxu; Feng, Yuan; Zhao, Xuesheng; Sun, Chunjian; Hong, Feng; Liu, Chao

doi:10.1109/access.2019.2955957

Cited by 60 publications

(35 citation statements)

References 29 publications

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“…CNN models in particular have shown a better performance than other available numerical models for assessing SST. This technique is also extremely useful approach for estimating ENSO phenomena or prediction of sub-surface temperature or filling missing Argo data (Ham et al 2019;Han et al 2019). vi.…”

Section: Discussionmentioning

confidence: 99%

“…As mentioned earlier, deep learning based models such as "classic" LSTM, CNN and their improved versions have attracted significant attention lately within the research community in this field, with studies comparing classic LSTM and CNN for predicting SST in different regions (Han et al 2019;Wolff et al 2020) and other researchers focusing on enhancing the performance of classic CNN and comparing it with other soft computing models (Barth et al 2020;Saha and Chauhan 2020;Yu et al 2020b;Zhang et al 2020b).…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

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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

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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.

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Section: Discussionmentioning

confidence: 99%

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

show abstract

“…In Equation (2), where x is the sample eigenvalue, μ is the sample mean, σ is the standard deviation of the sample data, and X is the normalised eigenvalue [ 16 ].…”

Section: Study Areas and Materialsmentioning

confidence: 99%

A New Machine Learning Algorithm for Numerical Prediction of Near-Earth Environment Sensors along the Inland of East Antarctica

Wang

Dou

Yang

et al. 2021

Sensors

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Accurate short-term small-area meteorological forecasts are essential to ensure the safety of operations and equipment operations in the Antarctic interior. This study proposes a deep learning-based multi-input neural network model to address this problem. The newly proposed model is predicted by combining a stacked autoencoder and a long- and short-term memory network. The self-stacking autoencoder maximises the features and removes redundancy from the target weather station’s sensor data and extracts temporal features from the sensor data using a long- and short-term memory network. The proposed new model evaluates the prediction performance and generalisation capability at four observation sites at different East Antarctic latitudes (including the Antarctic maximum and the coastal region). The performance of five deep learning networks is compared through five evaluation metrics, and the optimal form of input combination is discussed. The results show that the prediction capability of the model outperforms the other models. It provides a new method for short-term meteorological prediction in a small inland Antarctic region.

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“…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

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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.

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A Convolutional Neural Network Using Surface Data to Predict Subsurface Temperatures in the Pacific Ocean

Cited by 60 publications

References 29 publications

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

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

A New Machine Learning Algorithm for Numerical Prediction of Near-Earth Environment Sensors along the Inland of East Antarctica

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

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