How AI Will Revolutionize Fire Defense

Wildfires are growing in numbers and intensity, and they’re causing more damage. During the first half of last year, damages in the U.S. soared to $101 billion, including the loss of power stations, hospitals, communications systems and water supply systems. 

We have sufficient numbers of trained firefighters, but the current technology just isn’t enough to handle the levels of danger we see in today’s fires. 

The problem

The numbers of fires and the acreage burned is staggering. The Center for Disaster Philanthropy says that “as of Aug. 21, 2025, more than 3,997,080 acres have burned in the U.S. this year, in 44,470 fires.”

These fires are part of a broader trend where we see an increased risk of fire – and loss. Changing fire patterns are causing serious problems for the climate, our buildings and roads, public health and our economy. 

Shockingly, despite the increased risk to personal safety and the economy, the tools we use to fight them have not changed for more than fifty years.

Firefighting ops: A primer

Firefighting operations are complex and require participation from multiple levels. The first: the firefighter on the front lines – and there may be several of them – using hoses and nozzles to distribute the water. A battalion is composed of a group of engines, which is overseen by the battalion chief who allocates resources. The last level is the main control center, which might send several battalions to fight a fire and even seek support from firefighting aircraft when necessary.

However, pump operators still determine water pressure by hand, and nozzles continue to distribute an uneven flow of water. That translates into wasted water, fatigue, reduced effectiveness in extinguishing the fire and higher risk of injury due to inconsistent water pressure spikes.

Further, this outdated method doesn’t produce any data, leaving fire chiefs in the dark about how well their teams are performing and whether their suppression efforts are working.

Manual firefighting and its challenges

There are significant limitations with current fire suppression models, because they rely on manual calculations amidst high-pressure situations: firefighters don’t have information on ideal flow rates; and command personnel distribute resources without a true analysis of the fire’s behavior or water supply levels. Without predictive tools, it’s exponentially more difficult to keep up with new threats. 

The missing link with hardware alone

The focus on firefighting equipment has historically been on the mechanics and how it works rather than how “smart” it was. Consequently, pump operators had to change pressure manually while simultaneously monitoring gauges in critical situations. Without insight into flow rates and nozzle performance, firefighters are left to figure out complicated fluid dynamics in their heads while standing in front of a raging fire.

An improved model: Predictive, connected, autonomous

Data is king, especially when it comes to fire suppression; it offers key details about each engine’s water flow and pressure; available water levels; which hoses are being utilized; and the efficacy of water application. While this data is beneficial to battalion chiefs amidst complicated situations, it’s no longer enough. 

Enter prescriptive analytics. They’re used in fuel maps, GIS and weather applications and can offer critical insights ahead of time, like alerting firefighters that water will run out; if the equipment is likely to fail; and offer estimates on how the fire may spread based on current strategies. Fire departments can prepare in advance rather than simply reacting to emergencies.

In the future, prescriptive analytics will suggest ways to use resources effectively. Reinforcement learning will help systems figure out the best positions for each engine, determine the right flow rates and find the fastest way to put out a fire while using the least amount of water. Based on historic data, we believe that prescriptive analytics could cut water use by 50 percent and double the effectiveness of fire suppression efforts. 

Changing how we respond to fires: Predict, deploy, suppress

Traditional firefighting equipment is simply not enough anymore. Data is changing everything, and a new approach to firefighting – predict, deploy, suppress – will transform how we battle fires. 

Predict: From reactive to proactive

This stage changes fire response from reacting to the emergency to preparing for it in advance. By utilizing information from linked systems, we go from just looking at past data to gaining real-time insights.

• Smart AI models study pressure changes and the flow of fluids in the engine’s hydraulic systems. This replaces the “mental math” that pump operators do now with accurate, physics-based calculations. 

• Resource forecasting helps predict when an engine will run out of water. By looking at how quickly water is used, commanders can know in advance when they need to find additional water sources – before the tank runs dry. 
• Predictive maintenance algorithms help identify equipment problems, like a broken pump seal or valve, weeks before they lead to a breakdown during a fire response. This helps responders steer clear of the hidden problems that frequently weaken legacy systems.

Deploy: Immediate response

The “deploy” phase uses the data gathered in the “predict” phase to create an immediate response. It serves as the main control center at the fire scene, unifying parts that historically worked in separate siloes.  

• Dynamic resource allocation means that components like water flow, pressure and nozzle are changed in real-time to match what is expected during a fire. When a fire grows, the system can suggest or automatically change the pressure to provide the necessary force to extinguish it.
• A decision support layer reduces the significant mental effort necessary for manual calculations. In rapidly changing situations, it addresses the question: “Where is the next engine most needed?” 
• Adaptive control incorporates new information and enables the system to quickly adjust. As the wind shifts, or when a hose line is turned off, the strategy changes in real-time to maintain safety and efficiency.

Suppress: Impact precision

The information collected during the “predict” and “deploy” stages comes together to quickly and effectively put fires out while using the least amount of resources. 

• Enhanced delivery: This shifts the traditional approach of “surround and drown,” which generates waste and causes unnecessary damage, to providing the right amount of water and pressure necessary to extinguish the fire.  
• Real-time feedback: Sensors gauge the success of suppression efforts relative to temperature changes and fire line strength. The feedback system readjusts and offers alternatives to current flow rates or attack angles. 
• The process is controlled via feedback within an automated closed-loop system, which continually monitors its own performance and adjusts accordingly. Ultimately, the goal is to improve efficiency and accuracy by ensuring that the efforts to put out the fire are always ahead of it. 

The bottom line  

Collecting data turns the fire engine from a machine into a smart system that uses sensors, machine learning and real-time analysis to provide critical strategic insights. This establishes a new level of operational awareness and a system for modern fire protection. 

Firefighters can change how they fight fires by using data and AI, enabling them to both measure their success and transform fire suppression methods.