High-resolution temperature and salinity model analysis using support vector regression

Jiang, Yu; Zhang, Tong; Gou, Yu; He, Lin; Bai, Hongliang; Chen, Hu

doi:10.1007/s12652-018-0896-y

Cited by 23 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another study, Jiang et al (2018b) evaluated the SVR and LR performance for SST prediction in the Canadian Berkley Canyon. Latitude, longitude, and water depth were used as input variables.…”

Section: Overview Of the Other Available Soft Computing Models For Ssmentioning

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

Section: Overview Of the Other Available Soft Computing Models For Ssmentioning

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

“…There are several conventional methods, including linear regression, logistic regression, ridge regression, and support vector regression. Jiang et al exploit SVR for regressive predictive analysis [16]. Gou et al apply the KNN regression algorithm to predict ocean temperature and salinity [17].…”

Section: Related Workmentioning

confidence: 99%

DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction

Gou

Zhang

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

The emerging Internet of Underwater Things (IoUT) and deep learning technologies are combined to provide a novel, intelligent, and efficient data processing and analyzing schema, which facilitates the sensing and computing abilities for the smart ocean. The underwater acoustic (UWA) communication network is an essential part of IoUT. The thermocline, in which temperature and density change drastically, affects the connectivity and communication performance between IoUT nodes, as well as the network topologies. In this paper, we propose DeepOcean, a deep learning framework for spatio-temporal ocean sensing data prediction, which consists of a generative module and a prediction module. We implement the generative module with a multi-layer perceptron (MLP) to capture the spatial dependencies and construct high-resolution data based on sparse observations. The prediction module is implemented with our proposed Multivariate Convolutional LSTM (MVC-LSTM) neural network, which captures both the spatio-temporal dependencies and the interactions of different oceanographic features for prediction. We evaluate the effectiveness of DeepOcean with Argo data, where the proposed framework outperforms fifteen state-of-art baselines in terms of accuracy. INDEX TERMS Internet of Underwater Things (IoUT), deep learning, spatio-temporal prediction, multivariate convolutional LSTM (MVC-LSTM) neural network, thermocline.

show abstract

“…Information entropy: an indicator to measure the purity of the collective samples. This paper combines the 'information entropy method' [Jiang, Zhang, Gou et al (2018)] in machine learning with the traditional method for more precise determination Suppose that the proportion of the first k classes in the dataset is D, and…”

Section: Preliminariesmentioning

confidence: 99%

Few-shot Learning with Generative Adversarial Networks Based on WOA13 Data

Li¹,

Liang²,

Zhao³

et al. 2019

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

In recent years, extreme weather events accompanying the global warming have occurred frequently, which brought significant impact on national economic and social development. The ocean is an important member of the climate system and plays an important role in the occurrence of climate anomalies. With continuous improvement of sensor technology, we use sensors to acquire the ocean data for the study on resource detection and disaster prevention, etc. However, the data acquired by the sensor is not enough to be used directly by researchers, so we use the Generative Adversarial Network (GAN) to enhance the ocean data. We use GAN to process WOA13 dataset and use ResNet to determine if there is a thermocline layer in a sea area. We compare the classification results of the enhanced datasets of different orders of magnitude with the classification results of the original datasets. The experimental result shows that the dataset processed by GAN has a higher accuracy. GAN has a certain enhancement effect to marine data. Gan increased the accuracy of the WOA dataset from 0.91 to 0.93. At the same time, the experimental results also show that too much data cannot continue to enhance the accuracy of WOA in ResNet.

show abstract

High-resolution temperature and salinity model analysis using support vector regression

Cited by 23 publications

References 18 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

DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction

Few-shot Learning with Generative Adversarial Networks Based on WOA13 Data

Contact Info

Product

Resources

About