Natural and man-made hazards evolve rapidly alongside political changes and emerging technologies and can be exacerbated by climate change. Before hazards materialize into disasters that damage life and property, emergency management (EM) teams must learn to optimize their hazard response capabilities and tools to act swiftly and strategically in a three-dimensional environment that spans speed, distance, and time. More importantly, EM personnel need to digest and manage large volumes of data from various outlets during a crisis to make the best, informed decision. Artificial intelligence (AI) and machine learning (ML) may help manage these data. LMI believes AI/ML can help reduce loss of and damage to life, property, and resources by delivering early detection and warning, enhancing risk assessments, enabling rapid and strategic decision-making, improving coordination with other agencies, and bolstering protection for first responders. With experience providing AI/ML planning, architectural, and management solutions to federal agencies like the Department of Homeland Security (DHS) and Department of Defense (DoD), LMI is knowledgeable and equipped to integrate AI/ML solutions to improve the EM sector’s predictive and prescriptive capabilities.

Artificial Intelligence Defined

AI is the broad concept of machines making decisions and carrying out tasks in ways that humans normally would. ML is the application of AI based on the idea that machines can be empowered to learn by themselves (e.g. recognizing patterns within a dataset) when provided with the right data—often learning quicker or better than humans could. Together, AI/ML applications efficiently harness and analyze data to assist EM teams with critical decision-making. As Ron Fizer, LMI Management Strategy Fellow and CWMD subject matter expert, notes, “leveraging AI/ML reduces decision-making time, simultaneously increases confidence, and helps identify the resources that are needed to mitigate consequences.” LMI’s AI/ML approach familiarizes clients with modern EM tools and technologies and hones prioritization and critical decision-making for all hazards.

Acknowledging the role and advantage of AI/ML in hazard response is the first step to understanding its function and relevance—it is also worth noting how AI/ML applications are being used to augment existing EM tools. For example, the modeling and simulation (M&S) community uses live, virtual, and constructive (LVC) simulations to better represent humans in operational analyses, especially in hazardous environments. LVC simulations are cost-effective and save time, conserving funds normally used to reproduce physical training facilities and expensive equipment. Rather than manually interpreting separate LVC outcomes, EM teams could integrate AI/ML applications with LVC simulations to generate more meaningful, contextual output and further produce synthetic data to train AI/ML on complex issues and ever-changing threats. In this way, AI/ML acts as a force multiplier, amplifying the type and quality of results that participants and stakeholders subsequently feed into their decision-making process.

Using Artificial Intelligence

AI/ML applications can also be applied using decision trees—ML algorithms that divide a given dataset into smaller portions until it can predict a quantity (regression trees) or predict a class/label (classification trees)—to provide the most informed outcome. However, there are challenges to ensuring AI/ML are applied appropriately. For instance, AI/ML applications need to consider the type of data being fed into decision trees. Training data used for ML models should be plentiful and high-quality (clean, labelled, and structured) to minimize biases and errors and maximize the generalizability of the model outcomes. In addition, AI/ML applications need to achieve certain levels of interoperability and integrability to have macro-level effects in hazard response. Highly interoperable and integrable AI/ML applications can streamline effort across different systems. The threshold for such usage has not yet been determined, but constantly monitoring models will help ensure the AI/ML application is performing according to standards and expectations. Although there is concern that AI/ML programs might replace humans in vital decision-making processes, LMI understands that AI/ML solutions do not stand independently. AI algorithms are created, used, and monitored by personnel and require human supervision and manual correction.

Artificial Intelligence for Disaster Response

These challenges should not deter anyone from considering or using AI/ML for hazard response. Whether applied to LVC simulations or real-time hazard situations, AI/ML helps produce purposeful and intelligible interpretations of data and frees personnel to tackle the critical resourcing and tactical decisions essential to saving lives and mitigating the effects of disasters. AI/ML applications necessitate a sharpened focus on identifying, detecting, planning, training, and analyzing vulnerabilities that is not limited to any one government or sector. Ray Compton, LMI Solutions Architect Fellow and modeling and simulation subject matter expert, notes that, “AI/ML applications are cross cutting—they can be applied across various industries or be used by numerous federal agencies to increase personnel proficiencies to respond to situations and guide probable decisions.”

LMI embraces these benefits and challenges in its AI/ML approach and will be providing project support to the National Urban Security Technology Laboratory’s (NUSTL) System Assessment and Validation for Emergency Responders (SAVER) program, also known as AI Patient Triage. This project will focus on the desire of first responder agencies to leverage technology—such as data driven dispatch tools that can assist with preliminary diagnostic information, identify appropriate and available resources, expedite services being rendered, and potentially alleviate capacity issues within emergency rooms and hospitals—to influence patient outcomes and predict patient needs.

The interest in applying AI/ML solutions to improve patient care and patient prioritization systems demonstrates one of many ways AI/ML can positively augment and modernize hazard response capabilities. LMI recognizes the importance of understanding and harnessing AI/ML in today’s technologically advanced world and is ready to administer innovative solutions to help clients address threats in the EM and Countering Weapons of Mass Destruction realm.

Our Experts

Raymond Compton

Raymond Compton

Fellow, Solutions Architecture Meet Raymond

Raymond Compton

Fellow, Solutions Architecture

Ray Compton, a retired U.S. Army colonel, joined LMI in 2019 and serves as a principal for National Security Science & Technology, supporting internal and external LMI stakeholders in the strategic development of integrated solutions for capability gaps in national defense.

Ron Fizer Headshot

Ron Fizer

Fellow Emeritus, Management Strategy Meet Ron

Ron Fizer

Fellow Emeritus, Management Strategy

Ron Fizer, a retired U.S. Army colonel, applies his technical and analytical expertise to complex problems related to countering weapons of mass destruction (CWMD) and chemical, biological, radiological, and nuclear (CBRN) defense.

Keith Rodgers Headshot

Keith Rodgers

Sr. Vice President, Digital & Analytic Solutions Meet Keith

Keith Rodgers

Sr. Vice President, Digital & Analytic Solutions

Keith brings nearly two decades of experience in leveraging innovative techniques to assess organizational performance and challenges.