{"id":1941,"date":"2018-11-29T00:00:00","date_gmt":"2018-11-29T00:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/blog\/using-ai-and-iot-for-disaster-management"},"modified":"2023-05-11T15:35:45","modified_gmt":"2023-05-11T22:35:45","slug":"using-ai-and-iot-for-disaster-management","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/using-ai-and-iot-for-disaster-management\/","title":{"rendered":"Using AI and IoT for disaster management"},"content":{"rendered":"

In countries around the world, natural disasters have been much in the news. If you had a hunch such calamities were increasing, you\u2019re right. In 2017, hurricanes, earthquakes, and wildfires cost $306 billion worldwide<\/a>, nearly double 2016\u2019s losses of $188 billion.<\/p>\n

Natural disasters caused by climate change, extreme weather, and aging and poorly designed infrastructure, among other risks, represent a significant risk to human life and communities. Globally, $94 trillion in new investment<\/a> is needed to keep pace with population growth, with a large portion of that going toward repair of the built environment. These projects have long cycles due to government authorization processes, huge financial investments, and multi-year building efforts. We need to think creatively about how to accelerate these processes now.<\/p>\n

\"IoT<\/p>\n

National, state, and local governments and organizations are also grappling with how to update disaster management practices to keep up. The Internet of Things (IoT), artificial intelligence (AI), and machine learning can help. These technologies can improve readiness and lessen the human and infrastructure costs of major events when they do occur. Disaster modeling is an important start and can help shape comprehensive programs to reduce disasters and respond to them effectively.<\/p>\n

Anticipating disasters with better data<\/h2>\n

Fortunately, the science of predicting what\u2019s coming keeps getting better, enabling federal agencies\u2014FEMA, NASA, NOAA\u2014and municipalities to prepare. Organizations already use sensor data, LoRa devices, wireless radio frequency technology, and satellite imagery to predict the impact of disasters<\/a>. For example, disaster management teams could monitor IoT networks of weather base stations in the Caribbean as an early warning system for hurricanes and tropical storms and sensors on trees for drought conditions that increase the risk of forest fires<\/a>. SkyAlert, an early warning system in Mexico, uses a mobile app, standalone devices, and an IoT solution that runs on Microsoft Azure to provide alerts to millions of residents up to two minutes before a quake hits<\/a>, enabling them to move swiftly to safety.<\/p>\n

Preparedness at a granular level<\/h2>\n

IoT sensors and devices that are embedded in infrastructure assets make it possible for public safety officials and development planners to monitor data on roads, bridges, buildings, energy grids, and public transportation\u2014in real time. They can prioritize preventive maintenance and repairs; evaluate whether structures can withstand a coming weather event while continuing normal operations; and close unsafe assets. IoT could make sudden failures like bridge collapses\u2014and the corresponding loss of life and mobility chokepoints\u2014a thing of the past.<\/p>\n

Government agencies and localities can also apply AI and machine learning to IoT data sets to predict disaster impacts, so they can identify staging areas, evacuation routes, and flood areas<\/a>. Such information helps organizations marshal response efforts such as Duke Energy did when it staged 20,000 professionals across the Carolinas to respond to Hurricane Florence.<\/p>\n

During a crisis, IoT technology can help by continually updating which evacuation routes are no longer available and what transit options are up and running, for safer, faster mass people movement. Say there\u2019s a fire in a building or a stadium: IoT-powered systems can help direct individuals to all approved exits, while providing updates on which to avoid.<\/p>\n

Responding more efficiently<\/h2>\n

The first 72 hours of a disaster\u2019s aftermath are crucial. Emergency management teams must coordinate, set up operations, search for survivors, and take steps to minimize environmental crises such as chemical contamination. AI and IoT technologies underpin much of this initial response aggregating and analyzing data such as:<\/p>\n