Using Dynamic Data Driven Cyberinfrastructure for Next Generation Disaster Intelligence

Abstract

Wildfires and related disasters are increasing globally, making highly destructive megafires a part of our lives more frequently. A common observation across these large events is that fire behavior is changing, making applied datadriven fire research more important and time critical. Significant improvements towards modeling wildland fires and the dynamics of fire related environmental hazards and socio-economic impacts can be made through intelligent integration of modern data and computing technologies with techniques for data management, machine learning and artificial intelligence. However, there are many challenges and opportunities in integration of the scientific discoveries and datadriven methods for hazards with the advances in technology and computing in a way that provides and enables different modalities of sensing and computing. The WIFIRE cyberinfrastructure took the first steps to tackle this problem with a goal to create an integrated infrastructure, data and visualization services, and workflows for wildfire mitigation, monitoring, simulation, and response. Today, WIFIRE provides an end-to-end management infrastructure from the data sensing and collection to artificial intelligence and dynamic data-driven modeling efforts using a continuum of computing methods that integrate edge, cloud, and high-performance computing. Through this cyberinfrastructure, the WIFIRE project provides data driven knowledge for a wide range of public and private sector users, enabling scientific, municipal, and educational use. This paper summarizes the talk reviewing our recent work on building this dynamic data driven cyberinfrastructure and impactful application solution architectures that showcase integration of a variety of existing technologies and collaborative expertise.