Smart Sensor Networks with CNN, ANN, Deep Learning, and Random Forest for Predictive Risk Management in Iraq’s Oil Facilities

Sarmad Hamad Ibrahim Alfarag

Vol. 11, Issue 1, Jan-Dec 2025

Page Number: 41 - 61

Abstract:

Iraq’s oil installations are increasingly exposed to operational and environmental risks, stemming from ageing infrastructure, limited technology updates, and complex industrial settings. These challenges underscore the pressing requirement for smart systems that support real-time monitoring, early risk identification, and predictive maintenance. This work answers that need by suggesting a hybrid model between Smart Sensor Networks (SSNs), and intelligent algorithms such as, Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Deep Learning (DL), and Random Forest (RF) approaches to reduce the risk of failure in Iraqi oil infrastructure. The novelty of the present research consists in that it can turn former risk-based monitoring systems into proactive, preventive and predictive systems. AI-based platforms that can help reduce accidents and enhance system reliability.

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