In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factors with disaster resilience. Meanwhile, there is a lack of computationally rigorous, user-friendly tools that can support customized resilience assessment considering local conditions. This study aims to address these gaps through the power of CyberGIS with three objectives: 1) To develop an empirically validated disaster resilience model - Customized Resilience Inference Measurement designed for multi-scale community resilience assessment and influential socioeconomic factors identification, 2) To implement a Platform for Resilience Inference Measurement and Enhancement module in the CyberGISX platform backed by high-performance computing, 3) To demonstrate the utility of PRIME through a representative study. CRIM generates vulnerability, adaptability, and overall resilience scores derived from empirical hazard parameters. Computationally intensive Machine Learning methods are employed to explain the intricate relationships between these scores and socioeconomic driving factors. PRIME provides a web-based notebook interface guiding users to select study areas, configure parameters, calculate and geo-visualize resilience scores, and interpret socioeconomic factors shaping resilience capacities. A representative study showcases the efficiency of the platform while explaining how the visual results obtained may be interpreted. The essence of this work lies in its comprehensive architecture that encapsulates the requisite data, analytical and geo-visualization functions, and ML models for resilience assessment.

Disaster resilience is an area ripe for the use of artificial intelligence technologies. While some governments, companies and universities are using AI for this work, most efforts are in the early stages. AI technologies could help governments more quickly and efficiently identify risks, predict disasters and assess damage during and after an event. Recognizing the benefits of AI for disaster resilience in states at risk of natural disasters, such as California, the Partnership for Public Service worked with Microsoft to examine ways AI could help the federal, state and local governments improve their resilience. Read the findings in our white paper, "Into the Storm: Using artificial intelligence to improve California's disaster resilience."

Artificial intelligence could play a critical role in all phases of disaster resilience, according to "Into the Storm: Using Artificial Intelligence to Improve California's Disaster Resilience," an issue brief the Partnership released with Microsoft in early July. The brief explores the AI tools governments can use for disaster resilience and highlights how agencies are using AI technologies in the field. Considering that the Federal Emergency Management Agency has declared 27 major disasters across the United States in 2020 so far--not including those related to the COVID-19 pandemic--government officials need to think through how they can strengthen their ability to prepare for, respond to and recover from these shocks. Disasters put lives and livelihoods at risk, and governments at all levels--federal, state and local--must seek the best tools available to tackle the complex challenges involved and protect lives. At a release event held in July, Bijan Karimi, assistant deputy director for emergency services at the San Francisco Department of Emergency Management, and Stuart McKee, chief technology officer for state and local government at Microsoft, discussed basic principles disaster resilience officials should keep in mind when considering the use of AI.

NASA-funded researchers applied artificial intelligence to Facebook user location data captured as two fires wrecked northern California in 2018 and gained new insight into people's evacuation movements and behaviors when disaster strikes, which could strengthen future response. The Defense Innovation Unit and Carnegie Mellon University's Software Engineering Institute are collectively crafting datasets to teach AI tools to assess buildings and structures after natural crises occur, and ultimately augment and increase the accuracy of damage estimates. These are two of many examples detailed in a new report from the Partnership for Public Service and Microsoft that explores how the maturing technology can improve disaster resilience and response, and considerations and actions governments should pursue when adopting AI to boost preparedness, recovery and relief. The report suggests agencies improve data collection and access, make proactive instead of reactive moves, collaborate with other organizations--and more. "While some governments, companies and universities have already used AI in this field, most are still in the early stages of use," officials wrote in the report.

As urban populations grow, more people are exposed to the benefits and hazards of city life. One challenge for cities is managing the risk of disasters in a constantly changing built environment. Buildings, roads, and critical infrastructure need to be mapped frequently, accurately, and in enough detail to represent assets important to every community. Knowing where and how assets are vulnerable to damage or disruption by natural hazards is key to disaster risk management (DRM). The Global Facility for Disaster Reduction and Recovery (GFDRR) is a global partnership that provides knowledge, funding, and technical assistance towards achieving the vision of a world where resilient societies manage and adapt to ever-changing disaster and climate risk, and where the human and economic impact of disasters is reduced.