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| Establishment of Hull Steel Corrosion Rate Prediction Model Based on Deep Forest Algorithm |
| Received:February 18, 2025 Revised:March 10, 2025 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2025.04.015 |
| KeyWord:deep forest prediction model hull steel seawater corrosion |
| Author | Institution |
| ZHANG Penghui |
National Key Laboratory of Marine Corrosion and Protection, Luoyang Ship Material Research Institute, Shandong Qingdao , China |
| PENG Wenshan |
National Key Laboratory of Marine Corrosion and Protection, Luoyang Ship Material Research Institute, Shandong Qingdao , China |
| LIU Shaotong |
National Key Laboratory of Marine Corrosion and Protection, Luoyang Ship Material Research Institute, Shandong Qingdao , China |
| DING Kangkang |
National Key Laboratory of Marine Corrosion and Protection, Luoyang Ship Material Research Institute, Shandong Qingdao , China |
| HOU Jian |
National Key Laboratory of Marine Corrosion and Protection, Luoyang Ship Material Research Institute, Shandong Qingdao , China |
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| Abstract: |
| The work aims to study the application of the deep forest algorithm in predicting corrosion data of materials in seawater environments. In this paper, the principle of the deep forest algorithm was introduced, and applied to the establishment of a corrosion rate prediction model using the test data of hull steel obtained in China's offshore seawater to assess the performance of the model. Compared with the model constructed by applying the traditional neural network algorithm, the model constructed by applying the deep forest algorithm had higher performance and prediction accuracy, and had a very good generalization ability. The results showed that the model has good prediction accuracy and versatility, and can meet the application requirements for prediction of corrosion data of materials in seawater environments. |
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