Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Corer
The study of penetration depth of gravity piston samplers has an essential impact on sampling efficiency and instrument safety. This study focuses on predicting penetration depth based on the characteristic parameters of the sampled seafloor sediments and the sampler parameters. Although numerous st...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4457 |
| _version_ | 1797505603349774336 |
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| author | Xing Du Yongfu Sun Yupeng Song Qikun Zhou Zongxiang Xiu |
| author_facet | Xing Du Yongfu Sun Yupeng Song Qikun Zhou Zongxiang Xiu |
| author_sort | Xing Du |
| collection | DOAJ |
| description | The study of penetration depth of gravity piston samplers has an essential impact on sampling efficiency and instrument safety. This study focuses on predicting penetration depth based on the characteristic parameters of the sampled seafloor sediments and the sampler parameters. Although numerous studies of gravity corer penetration depth have been carried out, most have been based on the energy conservation equation, which considers a varying number of influencing factors. Furthermore, most research has focused on the same research idea of finding analytical solutions. The present study proposes a new approach to predicting gravity corer penetration depth based on a machine learning method that uses real sampling data from the sea and experimental data from a gravity sampling physical model for training and testing. Experimental results indicate that the machine learning model can accurately predict gravity corer penetration depth. Moreover, predictions were made for the same penetration conditions using the machine learning model and three other analytical solution models. Results show that the prediction accuracy of machine learning outperforms that of the analytical prediction model under various statistical rubrics. This study demonstrates the capacity of the proposed machine learning model and provides civil engineers with an effective tool to predict the penetration depth of gravity corers. |
| first_indexed | 2024-03-10T04:20:58Z |
| format | Article |
| id | doaj.art-7db93167d25e4a348be04479fd5bec6f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:20:58Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7db93167d25e4a348be04479fd5bec6f2023-11-23T07:49:18ZengMDPI AGApplied Sciences2076-34172022-04-01129445710.3390/app12094457Using a Machine Learning Method to Predict the Penetration Depth of a Gravity CorerXing Du0Yongfu Sun1Yupeng Song2Qikun Zhou3Zongxiang Xiu4Engineering Center, First Institute of Oceanography, MNR, Qingdao 266061, ChinaNational Deep Sea Center, Qingdao 266237, ChinaEngineering Center, First Institute of Oceanography, MNR, Qingdao 266061, ChinaEngineering Center, First Institute of Oceanography, MNR, Qingdao 266061, ChinaEngineering Center, First Institute of Oceanography, MNR, Qingdao 266061, ChinaThe study of penetration depth of gravity piston samplers has an essential impact on sampling efficiency and instrument safety. This study focuses on predicting penetration depth based on the characteristic parameters of the sampled seafloor sediments and the sampler parameters. Although numerous studies of gravity corer penetration depth have been carried out, most have been based on the energy conservation equation, which considers a varying number of influencing factors. Furthermore, most research has focused on the same research idea of finding analytical solutions. The present study proposes a new approach to predicting gravity corer penetration depth based on a machine learning method that uses real sampling data from the sea and experimental data from a gravity sampling physical model for training and testing. Experimental results indicate that the machine learning model can accurately predict gravity corer penetration depth. Moreover, predictions were made for the same penetration conditions using the machine learning model and three other analytical solution models. Results show that the prediction accuracy of machine learning outperforms that of the analytical prediction model under various statistical rubrics. This study demonstrates the capacity of the proposed machine learning model and provides civil engineers with an effective tool to predict the penetration depth of gravity corers.https://www.mdpi.com/2076-3417/12/9/4457gravity piston corerpenetration depthmachine learningartificial neural network |
| spellingShingle | Xing Du Yongfu Sun Yupeng Song Qikun Zhou Zongxiang Xiu Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Corer Applied Sciences gravity piston corer penetration depth machine learning artificial neural network |
| title | Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Corer |
| title_full | Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Corer |
| title_fullStr | Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Corer |
| title_full_unstemmed | Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Corer |
| title_short | Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Corer |
| title_sort | using a machine learning method to predict the penetration depth of a gravity corer |
| topic | gravity piston corer penetration depth machine learning artificial neural network |
| url | https://www.mdpi.com/2076-3417/12/9/4457 |
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