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

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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