AI for Disaster Response

Well before a natural disaster strikes, AI is being used to better predict threats, help first responders prioritize logistics, plan disaster responses, and even suggest ways of reducing the impact of future disasters. AI is enhancing our ability to prepare for and mitigate disasters before they occur by improving forecasts of hurricane tracks, tornados, floods, wildfires, and other weather threats.

Improving Wildfire Response

Google

Wildfires affect hundreds of thousands of people each year and are increasing in frequency and size. The need for accurate information when wildfires occur has never been greater. Google has partnered with a number of governments to develop a wildfire tracker that detects wildfire boundaries using new AI models based on satellite imagery to show their real-time location in Search and Maps. The tracker provides updated fire boundary information every 10–15 minutes and incorporates information from local authorities, on Google Search and Google Maps, allowing people to keep safe and stay informed about potential dangers near them, their homes, or loved ones.

 

Improving Natural Disaster Response

OpenStreetMap and Microsoft

Humanitarian OpenStreetMap Team (HOT) partnered with Microsoft and Bing to improve the mapping of areas vulnerable to natural disasters and poverty. By working with communities to document at-risk areas, HOT enables relief programs to respond more quickly and effectively after disasters. The program combines satellite mapping, machine learning, and an army of volunteers to create a new generation of detailed and potentially lifesaving maps.

 

Enhancing Flood Mitigation 

Google’s Flood Hub

Google’s AI-driven Flood Hub helps governments, aid organizations, and individuals take timely action and prepare for riverine floods, providing locally relevant flood data and forecasts up to 7 days in advance. Flood Hub’s AI uses diverse, publicly-available data sources, such as weather forecasts and satellite imagery. The technology then combines two models: the Hydrologic Model, which forecasts the amount of water flowing in a river, and the Inundation Model, which predicts what areas are going to be affected and how deep the water will be.

Flood Hub was created in 2018 to help combat the catastrophic damage from yearly floods in India and Bangladesh. Due to advances in their global AI and machine learning forecasting models, Google has been able to expand Flood Hub to over 80 countries, covering 460 million people globally.

 

Streamlining Disaster Response Operations

IBM’s ORI

IBM’s Operations Risk Insight (ORI) platform uses structured and unstructured data, including risk alert sources, vulnerability, susceptibility and resilience indicators, and news sources, and applies natural language processing and machine learning to visualize and communicate multi-hazard risks in real time and assist with decision-making. IBM and several nongovernmental organizations (NGOs) have partnered to improve and customize the platform for disaster response leaders. For example, ORI provides Day One Relief, Good360, and Save the Children with customized hurricane and storm alerts as well as layered data sets to generate map overlays to increase situational awareness, consolidate efforts, organize needs, and get potentially life-saving items to places that need them the most.