Corrosion Rate Prediction of Buried Oil and Gas Pipelines: A New Deep Learning Method Based on RF and IBWO-Optimized BiLSTM–GRU Combined Model
The corrosion of oil and gas pipelines represents a significant factor influencing the safety of these pipelines. The extant research on intelligent algorithms for assessing corrosion rates in pipelines has primarily focused on static evaluation methods, which are inadequate for providing a comprehe...
Saved in:
| Main Authors: | Jiong Wang, Zhi Kong, Jinrong Shan, Chuanjia Du, Chengjun Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/23/5824 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
by: Yunli Fan, et al.
Published: (2024-11-01) -
Sign Language Recognition Based on CNN-BiLSTM Using RF Signals
by: Yajun Zhang, et al.
Published: (2024-01-01) -
Research on Chinese predicate head recognition based on Highway-BiLSTM network
by: Ruizhang HUANG, et al.
Published: (2021-01-01) -
Monthly Precipitation Prediction Based on Attention-BiLSTM Model
by: CHENG Yuxiang, et al.
Published: (2024-06-01) -
Optimising Insider Threat Prediction: Exploring BiLSTM Networks and Sequential Features
by: Phavithra Manoharan, et al.
Published: (2024-11-01)