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Reinventing Disaster Response With AI and Data Modeling

Predictive modeling can help stimulate traffic flow, and when combined with a robust mass notification system can help emergency managers conduct efficient evacuation operations during disasters.

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Just as machine learning, artificial intelligence, data modeling and analytics platforms have transformed manufacturing, drug discovery, health care and operations in a host of other industries, these technologies and approaches are reinventing the way public agencies and even private enterprises manage disaster preparedness and response.

In the past, critical event modeling could only be funded and performed by federal agencies such as the National Oceanic and Atmospheric Administration and the National Hurricane Center or at large research universities. But now, with the ubiquity and affordability of cloud computing combined with the low cost of collecting and storing vast volumes of data, many types of modeling, including critical event modeling, are now broadly accessible.

Today, state, county and city agencies, as well as private enterprises, can use new technology to model wildfires, floods, and other critical events. These new tools allow emergency managers and response teams to shift from a reactive critical event management posture to a highly proactive stance.

These capabilities help emergency managers test and validate response plans before they have to be enacted. This gives emergency managers confidence that they will be ready when it matters and able to act with the confidence of knowing they have all the information they need to make the right decision.

Drawing on historical meteorological data, critical event models can help emergency managers predict the impact of disasters on their community under a variety of “what-if” scenarios. By experimenting ahead of time with hypothetical situations, emergency managers are less likely to be taken by surprise by actual events as they develop.

Beyond improving preparedness, critical event modeling can also help while an actual event is unfolding, enabling emergency managers to alter their responses as needed in real time to best address changing circumstances on the ground.

Predictive modeling can also simulate traffic flow during evacuations to better understand potential choke points and optimize evacuation routes. When combined with a robust mass notification system, predictive modeling helps emergency managers conduct efficient evacuation operations.

Predictive simulation and modeling have a vital role to play in assisting emergency personnel in managing complex, high-stakes, and high-pressure situations. But in the heat of the moment, the actual decision-making lies with accomplished emergency managers and first responders.

No technology solution can (or should attempt to) replace that invaluable experience and expertise. But in any critical event, time is of the essence. If predictive modeling can help emergency managers be confident that they have all the data they need to make the right calls quickly, their duty of care to their constituents demands that they put this technology to use so they can be ready when it matters.

We live in an age where extreme weather, natural disasters, and other tragedies are increasing in frequency and severity. We’re also fortunate to live in an age where emergent technology provides new tools that allow us to better prepare for and reduce the impact of catastrophic events so that we may lead happier, healthier, safer lives.

Richard Danforth is CEO of Genasys. He brings more than 30 years of defense and aerospace industry experience to the protective communications sector. As a highly effective, resourceful and innovative global business leader, Danforth has successfully led sophisticated product development and manufacturing organizations.