The world is becoming more unpredictable, with frequent and severe natural disasters, pandemics, and human-made crises. Besides the persistent global health crisis, the US has encountered an unmatched series of natural disasters in the past few years. Between 2016 and 2022, there have been 122 distinct disasters with damages exceeding one billion dollars each. Amidst this, emergency assistance and disaster recovery efforts are pushed beyond their limits. The traditional methods of emergency management are increasingly overwhelmed in the face of evolving threats and limited resources.  

 Artificial Intelligence (AI) and Machine Learning (ML) offer powerful tools to augment the capabilities of the emergency assistance workforce and improve the outcomes of disaster response and recovery efforts. The technology is improving at breakneck speeds. TechSur Solutions envisions and plans for a world where the Emergency Management workforce can use Grants Management systems that are fully optimized, secure, and efficient. AI/ML can empower Emergency Management agencies (such as the DHS Federal Emergency Management Agency) to make risk-informed decisions and respond rapidly to emergencies for a Prepared Nation.  

Applications of Artificial Intelligence and Machine Learning in Emergency Management 

Image: (December 14, 2021) MAYFIELD, Ky. — Louisville Emergency Management Director E.J. Meiman and Administrator Criswell discuss response operations near the Mayfield Consumer Products factory that was destroyed by a tornado late Friday. 

 The global market for incident and emergency management is projected to expand from an estimated USD 121.4 billion in 2022 to USD 163.6 billion by 2027, representing a Compound Annual Growth Rate (CAGR) of 6.2%. The market is expected to receive a significant boost from the escalating frequency of natural disasters worldwide. In these challenging times, AI/ML can play a role in enhancing emergency management efficiency in the following ways. 

Resource Allocation

AI can help allocate critical resources, such as food, water, and medical supplies, to the most vulnerable and impacted locations. In order to optimize resource allocation in real-time, Machine Learning may examine past data on resource requirements and consumption trends. Hurricane Harvey in 2017 was one event that led to the successful implementation of a similar strategy to locate vulnerable regions.

Disaster Response

Analysis of real-time data from numerous sources, such as social media, sensors, and satellites is possible with AI. This can help pinpoint regions of damage, gauge the impact, and set priorities for response actions. During the California wildfires, the WiFire Lab developed an AI system called BurnPro 3D that used real-time data to predict the spread of the fire, allowing firefighters to prioritize their efforts and adjust their strategies in real-time.

Recovery Efforts

Artificial Intelligence makes it easier to prioritize recovery operations based on the most pressing needs. It further helps estimate the cost of recovery and assess the extent of damage. For example, an AI system, xView2, analyzed satellite imagery to identify and estimate the extent of damage caused by the Turkey-Syria earthquake, allowing emergency responders to prioritize recovery efforts and allocate resources more effectively.

Benefits of Incorporating Artificial Intelligence and Machine Learning  

Image: JENNINGS, La. (Oct. 11, 2020) — FEMA Administrator Pete Gaynor talks about the whole-of-government response and recovery efforts after Hurricane Delta during an interview with The Weather Channel. FEMA photo by Jocelyn Augustino. 

 Integrating Al/ML into the training and skill enhancement of emergency management personnel can yield multiple advantages, including the following.

• Improved Situational Awareness: Artificial Intelligence can give emergency management experts real-time alerts and insights. This  empowers them to make quick, well-informed decisions. 
• Better Decision-Making: Machine Learning enables emergency management professionals to make data-driven decisions. It is possible by analyzing massive amounts of data and providing insights into patterns and trends. 
• Efficiency Gain: AI can automate routine processes like data collection and analysis. This allows emergency management specialists to concentrate on life-saving duties. 
• Reduced Risk of Human Error: In complicated and time-sensitive emergency management scenarios, such as resource allocation and response planning, AI can reduce the risk of human error. 

Successful Artificial Intelligence and Machine Learning Implementations in Emergency Management 

 DHS Federal Emergency Management Agency (FEMA) needed a resilient, modern system. This was crucial to evaluate each property and assess its flood risk based on data from hundreds of sources. They collected meteorological, geospatial, and other types of information. The agency gained valuable insights for faster and more accurate decision-making regarding flood insurance policies. TechSur Solutions created a modern, intelligent, DevSecOps-driven system for FEMA’s National Flood Insurance Program’s PIVOT program. Such experience empowers TechSur to take FEMA systems to the next level with our AI/ML expertise.  

 Here are a few examples of AI/ML solutions serving the Emergency Management sector:  

University of California & FEMA AI-Based System

Recent studies have demonstrated the potential of Artificial Intelligence and Machine Learning in enhancing disaster preparedness efforts. Artificial Intelligence can enable more precise predictions of the potential impact of a disaster on the affected population and infrastructure. AI specialists achieve this by analyzing and interpreting large amounts of data related to natural disasters. Artificial Intelligence can generate models that identify high-risk areas and recommend mitigation measures to minimize the impact of such disasters. An illustration of this is the AI-based system developed by researchers at the University of California, Berkeley, which increases accuracy in predicting the impact of earthquakes on buildings in California. The system employs machine learning algorithms to analyze seismic data. It provides comprehensive information about the anticipated intensity and duration of an earthquake. The extent of damage it would inflict on various types of structures can also be determined by the system.

Similarly, FEMA used machine learning to analyze data from social media and other sources to identify areas of damage and prioritize response efforts during Hurricane Maria in 2017. The analysis enabled FEMA to deploy resources more effectively and save lives. 

Copernicus Emergency Management Service

The European Union’s Copernicus Emergency Management Service uses Artificial Intelligence to analyze satellite images and other data to assess the impact of natural disasters, such as floods and earthquakes, and provide real-time information to emergency management specialists. The service has improved the speed and accuracy of response efforts and helped reduce the damage caused by disasters. 

UNDP’s MHEWS System

Currently, The United Nations Development Programme (UNDP) is developing a nationwide multi-hazard early warning system (MHEWS) in Georgia. The primary goal of this system is to reduce the exposure of communities, livelihoods, and infrastructure to weather- and climate-driven natural hazards. To achieve this, the MHEWS requires accurate forecasts and hazard maps of severe convective events, specifically hail and windstorms. The system will use AI/ML technologies to analyze data from various sources, such as weather stations and satellites, to provide timely and precise forecasts. It will also create hazard maps to identify the areas at the greatest risk. This would allow emergency management professionals to allocate resources more effectively and prioritize response efforts.  

Conclusion 

Artificial Intelligence and Machine Learning has enormous potential to empower the emergency management workforce. It can proficiently improve the outcomes of disaster response and recovery efforts. AI/ML enhances situational awareness, improves decision-making, increases efficiency, and reduces human error. This enables emergency management professionals to respond effectively to evolving threats and save lives. Federal government agencies and other organizations can greatly benefit from incorporating Artificial Intelligence and Machine Learning into their emergency management strategies and training programs.

TechSur Solutions leads AI/ML initiatives at several Federal agencies, including the Institute for Museum and Library Services (IMLS) (with an award-winning AI program) and DHS U.S. Citizenship and Immigration Services (USCIS). TechSur implements Artificial Intelligence at USCIS to predict operational issues before they occur to support users and improve immigration processing used by 19,000 government employees and contractors working at 223 offices worldwide. TechSur Solutions offers a comprehensive suite of services. This includes platform engineering, application modernization, and AI-driven analytics. These help transform Emergency Management Grants Systems into a secure, streamlined, and user-centric system that aligns with agency strategic goals. 

To learn more about how AI and machine learning can benefit your organization, contact TechSur, your agency’s partner in transforming emergency management. 

Image: (March 24, 2023) The Smithsonian Institution and FEMA Co-host a Disaster Simulation for Protecting Cultural Artifacts.

Hyper-automation integrates diverse advanced technologies to automate and optimize complex Enterprise activities. Benefits of hyper-automation include the consolidation of operations, significantly reduced overheads, and enhanced overall efficiency. It leverages the power of Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), and other tools to automate repetitive tasks and minimize errors. Read on to discover how our expertise in platform engineering, application modernization, and AI-driven analytics can revolutionize grants management systems, streamline processes, and enable data-driven decision-making.

Potential Impact of Hyper-Automation on Grants Management 

Image: (February 14, 2023) A Mitigation Assessment Team (MAT) traveled to Florida to learn about Hurricane Ian’s effects on the built environment.

In the context of grants management, hyper-automation plays a pivotal role in expediting and simplifying the grant management process. Grants management covers a wide range of processes and activities, including application review, data entry, budget tracking, and performance reporting. By automating these activities, public sector organizations can not only save valuable time and resources but also augment the precision and effectiveness of the overall process.

According to a report by Gartner, the adoption rate of structured automation in organizations is expected to increase from 20% in 2021 to 70% by 2025. This trend of automating processes for improved operational efficiency is referred to as “hyper-automation” and is the next evolutionary step in this domain.

Hyper-automation can simplify the grants management process in the following areas:

1. Intelligent Document Processing 

 A recent study conducted by PwC discovered that employing even basic AI-powered data extraction methods can result in a reduction of 30–40% in the time typically dedicated to such procedures by organizations. Utilizing advanced AI and Optical Character Recognition (OCR) technologies, hyper-automation enables rapid extraction, classification, and validation of data from grant applications and supporting documents. This significantly reduces manual data entry errors and accelerates the review process, demonstrating the transformative potential of automation in managing grants.

2. Streamlined Budget Tracking 

 Through automating the monitoring of budgets and costs, organizations can achieve considerable time and resource savings. Robotic Process Automation (RPA) can be used to automate budget development and monitoring, ensuring that organizations stay within their spending limits and don’t go overboard.

 Consider a scenario where an agency has received a grant to fund a particular project. The agency is required to submit regular reports on the project’s progress and budgetary expenditures to the grantor. With the help of automation, the agency can automate the process of tracking and monitoring its budget. This can involve setting up a dynamic workflow that automatically aggregates data related to the project’s expenses and evaluates them against the financial stipulations of the grant, thereby optimizing budget management and compliance.

3. Improved Performance Reporting and Evaluation 

 Hyper-automation can streamline the reporting process by automatically aggregating, analyzing, and visualizing grant performance data in real time. This empowers organizations to evaluate the impact of funded projects, identify areas for improvement, and make data-driven decisions to enhance future grant-making strategies.

4. Predictive Analytics and Decision Support 

 By leveraging ML algorithms and data analytics, hyper-automation can generate predictive insights and recommendations to guide decision-makers in evaluating grant applications and allocating resources. This can optimize the selection process by identifying high-potential projects, avoiding possible risks, and ensuring funds are utilized effectively.

5. Compliance Monitoring and Risk Management 

 With the integration of AI/ML technologies, hyper-automation can continuously monitor grant recipients’ compliance with grant terms and regulatory requirements. By identifying discrepancies and potential risks early on, organizations can proactively address issues, ensuring the successful execution of funded projects.

Hyper-Automation and DHS Agencies  

Image (January 13, 2023) C-SPAN: U.S. Fire Administrator Lori Moore-Merrell Announces Launch of National Fire Strategy (FEMA Photo)

A study by Deloitte suggested that adopting cognitive technologies like artificial intelligence (AI) has the potential to streamline U.S. federal government office operations, potentially saving up to 25% of work hours and cutting costs by as much as $41 billion.

Streamlining and improving business performance for DHS agencies such as the Federal Emergency Management Agency (FEMA) is crucial for optimizing operational efficiency, resource allocation, and decision-making in response to natural disasters and other emergencies. By leveraging advanced AI through hyper-automation, these agencies can better optimize the grant application, review, and disbursement processes, reducing manual intervention and improving accuracy and efficiency.

Moreover, streamlining processes, such as budget monitoring and performance reporting, enables seamless communication and coordination among various stakeholders, including federal, state, local, tribal, and territorial partners, as well as non-governmental organizations and the private sector. Enhanced collaboration not only speeds up response times but also minimizes duplication of efforts and reduces administrative overhead.

Ultimately, optimizing performance using hyper-automation for agencies like FEMA is critical to safeguarding public safety, ensuring resilience in the face of emergencies, and promoting the efficient use of taxpayer dollars. Hyper-automation can simplify the grants management process, resulting in a Ready FEMA that is better prepared to serve the nation.

Real-World Example of Hyper-Automation in Grants Management 

The Institute of Museum and Library Services (IMLS), an independent agency of the U.S. Federal government, provides library grants, museum grants, policy development, and research. TechSur Solutions implemented Adaptive Artificial Intelligence (AI) to deliver solutions that meet IMLS current needs and continues to grow with their evolving needs. We integrate AI technologies to drive advanced automated tasks with actionable results from core business processes to the added-value chain.

The Institute of Museum and Library Services (IMLS) First Check review process was originally performed by Program Officers going through application files and databases to validate a grants application manually, taking up to 2 weeks. TechSur’s First Check tool reviews hundreds of applications in 1-2 hours and has been implemented as the initial reviewer for grant applications. The tool automates 95% of the First Check rules which allows Program Officers to focus on validating tool’s results and performing rule checks outside of the tool’s scope. An internal website is included in the tool as a centralized space for viewing, validating, and exporting First Check results.

TechSur and IMLS’ First Check program recently won a Disruptive Tech award, which recognizes Federal IT programs that are working every day to take calculated risks and positively disrupt the Federal market. We look forward to celebrating all 2023 award winners at the 2023 #DisruptiveTechSummit on April 12, 2023. http://bit.ly/426jGXg @FedHealthIT.

Conclusion 

To make hyper-automation work for grant management, it’s essential to have everyone on board and create systems that support your automation journey. Even more important is having the right know-how and a smart plan to adopt automation technologies in your organization. By doing this, public sector organizations can tap into the true power of hyper-automation and make grant management more efficient and effective.

Looking to simplify your grants management processes and improve efficiency through hyper-automation? Get started on the path to a smarter, more resilient Grants Management System. Contact TechSur Solutions today for a consultation and see how our innovative solutions can help you achieve your strategic goals.