  With the rise in militant activity and rogue behaviour in oil and gas regions
around the world, oil pipeline disturbances is on the increase leading to huge
losses to multinational operators and the countries where such facilities
exist. However, this situation can be averted if adequate predictive monitoring
schemes are put in place. We propose in the first part of this paper, an
artificial intelligence predictive monitoring system capable of predictive
classification and pattern recognition of pipeline datasets. The predictive
system is based on a highly sparse predictive Deviant Learning Algorithm
(p-DLA) designed to synthesize a sequence of memory predictive clusters for
eventual monitoring, control and decision making. The DLA (p-DLA) is compared
with a popular machine learning algorithm, the Long Short-Term Memory (LSTM)
which is based on a temporal version of the standard feed-forward
back-propagation trained artificial neural networks (ANNs). The results of
simulations study show impressive results and validates the sparse memory
predictive approach which favours the sub-synthesis of a highly compressed and
low dimensional knowledge discovery and information prediction scheme. It also
shows that the proposed new approach is competitive with a well-known and
proven AI approach such as the LSTM.
