Could AI have prevented the Ramses telecommunications building fire?

Egypt Independent

2 days ago

The concept of a machine predicting an event or disaster, like that of fire which devastated the Ramses telecommunications building or even some natural disasters, might sound like something out of science fiction.
Yet its is now a serious topic of discussion- What if AI systems were applied to Egypt's critical infrastructure? Could they have predicted the danger before flames engulfed the network's heart? How exactly could AI predict or prevent disasters such as the Ramses telecommunications building fire?
This concept of 'crisis prediction' might seem like a distant technological luxury. In reality it could be a magic solution if properly applied, through using the latest technologies to predict risks before they escalate into catastrophes.
During the Ramses Exchange fire, which crippled a vital part of the communications sector, there was no early warning system, no alert to enable responsible authorities to act before the flames spread.
This is precisely where AI could play a major role.
AI and disaster prediction
'AI doesn't see the future, but it calculates its probabilities,' says AI expert Marco Mamdouh. He explained that while AI cannot literally predict the future, it relies using probability to analyze data and forecast potential scenarios.
Mamdouh explained that probability is taught as a fundamental branch within AI sciences.
If an AI system is fed accurate data about a building—its structural condition, equipment, and safety systems—it can analyze this information to conclude, for instance, that the building can withstand a limited fire but would be vulnerable to collapse or severe damage if a large-scale blaze erupted.
How does this actually work?
According to Mamdouh, AI can play a crucial proactive role by:
Analyzing structural weaknesses in vital buildings and facilities, providing a regular assessments of risk levels.
in vital buildings and facilities, providing a regular assessments of risk levels. Proposing crisis scenarios based on historical data from similar fires: duration, intensity, spread, and damage.
based on historical data from similar fires: duration, intensity, spread, and damage. Generating immediate automated solutions to confront the crisis, including evacuation options, strengthening protection measures, and prioritizing response actions.
From prediction to loss mitigation: Is the world implementing these measures?
'If equipped with sufficient data, AI can tell you whether a building can withstand specific thermal stress,' he added.
'Will the electrical system hold up in the event of a short circuit? Is there a need to update alarm systems or install additional sensors? AI answers all these questions before we're surprised by a crisis.'
He concluded by stating that AI isn't merely an analytical tool; it can serve as a strategic partner in decision-making. When a disaster strikes, rapid decision-making and prioritizing actions become crucial.
In these scenarios, smart systems can suggest the fastest ways to gain control, or even automatically shut down systems to minimize damage, much like what occurs in smart factories.
Global precedent: AI in action to prevent fires
South Korea stands as a real example of this concept's application, having launched AI-powered projects that analyze data from thousands of buildings and classify them by a 'fire risk index.'
Rapid response systems were then built for structures highest on the risk list.
In the US, municipalities like Atlanta developed AI-driven systems to identify which buildings required immediate inspection, contributing to a significant reduction in the number of fires within just one year.
A 2024 study published in the Fire scientific journal showcased researchers developing an advanced model for fire risk prediction using machine learning algorithms based on the Stacking Ensemble method.
The model analyzed 34 variables, including building age, type, materials used, and population density in the area, successfully classifying buildings by risk levels.
Remarkably, only 22 percent of the buildings in the highest risk category accounted for 54 percent of the fires actually recorded during the study period, demonstrating the model's accuracy and its ability to provide a realistic map of buildings prone to fire hazards.