How Artificial Intelligence is Changing Fire Fighting


Firefighters have a daring responsibility to brave the dangers of an inferno to save lives and protect the environment. This, of course, requires courage but also skill. Firefighting training is highly technical due to the nature of the hazards firefighters face and the complexity of navigating a building or environment engulfed in flames. The four major hazards they are confronted with include smoke, heat, inadequate oxygen and toxic atmospheres. Other important hazards are backdrafts (a situation in which oxygen is inadvertently introduced into an oxygen-depleted fire and exacerbates the conflagration),  falls, ongoing structural damage, and poor visibility. Most or all of these challenges are already being handled in one way or another.

Firefighters use a self-contained breathing apparatus (which holds at least 30 minutes of air)  to confront the challenge of smoke and the toxic atmosphere. They also wear personal protective equipment with fire-resistant clothing to guard against some of the heat.

A fire emergency is a high-pressure situation with very limited time available, thus necessitating sharp and calculated instinctive decision-making. Much of this skill is honed through training but the potential exists to improve firefighting capabilities through Artificial Intelligence.

Artificial Intelligence and Firefighting

Existing firefighting approaches can be significantly improved by AI’s capacity to learn from scenarios and process data using techniques such as machine learning. As the technology advances, so too will its peak and the range of use cases that it can be put to.

Some of the applications of AI in firefighting are as listed below:

Augmenting Firefighters: As firefighters often have to contend with life-threatening situations with factors such as poor visibility due to the smoke and weakening structures, their capacity to survive these conditions with the people they go in to rescue can be improved through AI augmenting technologies. Computer Vision, for example, can be used to outline the surrounding environment better for firefighters to give them an edge against the reduced visibility. Structural outlines can be better visualized, offering better navigation.

Carbon monoxide poisoning from smoke is another very important factor that firefighters have to contend with. Internet-of-Things-based solutions can be used with O2 saturation devices to track a firefighter’s O2 saturation in real-time and direct them on when to find safer breathing zones.

AUDREY: This is a specific use case being developed by NASA. It stands for Assistant for Understanding Data through Reasoning, Extraction, and Synthesis. AUDREY’s purpose is to analyze situational data and recommend safe rescue paths. AUDREY uses video and sensor information to determine how an ongoing fire can develop, the amount of time that responders have to conduct a rescue mission and the best possible mission rescue plans.

Responder Robots and Drones: Artificially intelligent robots such as drones can play a vital role as first responders in the case of an emergency. These devices can be controlled remotely to provide aerial and more in-depth situational analysis by accessing areas of a burning period that would otherwise be challenging for a human firefighter. By deploying these robots as immediate first responders, the chances of success are better improved.

Virtual Reality, Mixed Reality and Augmented Reality for training: Real-life scenarios are not easily or as safely replicated in training simulations for firefighters. However, using Augmented Reality technologies can simulate some of these conditions better and help firefighters prepare for the real-life situations. These technologies mimic reality enough to provide the trainee with the stakes involved in a similar situation and the crucial decisions that could mean the difference between life and death.

Predicting and controlling wildfires: This application is particularly important in the regions of the world where wildfires are common. It is also essential when prescribed fires are required in a given area. Automated reasoning and machine learning can be used to determine the most appropriate fire models for an area’s conditions and to better comprehend fire behavior. These bits of information can be used for wildfire predictive and management purposes.



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