Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness.
Pipeline corrosion has significant impacts on the human, economic, and natural environment. To help better detect and prevent it over time, in this paper, we propose a multivariate approach using machine learning. More precisely, we propose to study the evolution of the thickness of the mining pipel...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2024-05-01
|
| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135320 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849736812040290304 |
|---|---|
| author | Kalidou Moussa Sow Nadia Ghazzali |
| author_facet | Kalidou Moussa Sow Nadia Ghazzali |
| author_sort | Kalidou Moussa Sow |
| collection | DOAJ |
| description | Pipeline corrosion has significant impacts on the human,
economic, and natural environment. To help better
detect and prevent it over time, in this paper, we propose
a multivariate approach using machine learning.
More precisely, we propose to study the evolution of
the thickness of the mining pipeline using a multivariate
approach and to implement a predictive model using
the Long Short-Term Memory (LSTM) artificial neural
network. Indeed, LSTM is a specific recurrent neural
network (RNN) architecture designed to model temporal
sequences. The proposed predictive model achieved
an accuracy of 80% and a loss of 0.01 and was able to
predict variations in eight thickness measurements over
one hundred days. |
| format | Article |
| id | doaj-art-2dd3ff5a5a1c40f3aef8aac7905f32d1 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-2dd3ff5a5a1c40f3aef8aac7905f32d12025-08-20T03:07:10ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622024-05-013710.32473/flairs.37.1.13532071693Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness.Kalidou Moussa Sow0Nadia Ghazzali1Semantic, Logics, Information Extraction and AI (SLIE)University of Quebec at Trois-Rivieres, Department of Mathematics and Computer SciencePipeline corrosion has significant impacts on the human, economic, and natural environment. To help better detect and prevent it over time, in this paper, we propose a multivariate approach using machine learning. More precisely, we propose to study the evolution of the thickness of the mining pipeline using a multivariate approach and to implement a predictive model using the Long Short-Term Memory (LSTM) artificial neural network. Indeed, LSTM is a specific recurrent neural network (RNN) architecture designed to model temporal sequences. The proposed predictive model achieved an accuracy of 80% and a loss of 0.01 and was able to predict variations in eight thickness measurements over one hundred days.https://journals.flvc.org/FLAIRS/article/view/135320 |
| spellingShingle | Kalidou Moussa Sow Nadia Ghazzali Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness. Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| title | Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness. |
| title_full | Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness. |
| title_fullStr | Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness. |
| title_full_unstemmed | Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness. |
| title_short | Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness. |
| title_sort | developing a predictive model using multivariate analysis and long short term memory lstm to assess corrosion degradation in mining pipeline thickness |
| url | https://journals.flvc.org/FLAIRS/article/view/135320 |
| work_keys_str_mv | AT kalidoumoussasow developingapredictivemodelusingmultivariateanalysisandlongshorttermmemorylstmtoassesscorrosiondegradationinminingpipelinethickness AT nadiaghazzali developingapredictivemodelusingmultivariateanalysisandlongshorttermmemorylstmtoassesscorrosiondegradationinminingpipelinethickness |