Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness.

Authors

  • Kalidou Moussa Sow Semantic, Logics, Information Extraction and AI (SLIE)
  • Nadia Ghazzali University of Quebec at Trois-Rivieres, Department of Mathematics and Computer Science

DOI:

https://doi.org/10.32473/flairs.37.1.135320

Abstract

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.

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Published

13-05-2024

How to Cite

Sow, K. M., & Ghazzali, N. (2024). Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness. The International FLAIRS Conference Proceedings, 37(1). https://doi.org/10.32473/flairs.37.1.135320

Issue

Section

Special Track: Semantic, Logics, Information Extraction and AI