Towards an Integrated Performance Framework for Fire Science and Management Workflows
This work provides a building block for the National Data Platform, addressing performance needs in collaborative, real-time wildfire science workflows, but it appears incremental as it extends existing frameworks.
The paper tackles the problem of assessing and optimizing performance in scientific workflows for fire science and management by presenting an AI/ML approach, applied to the WIFIRE BurnPro3D platform for proactive fire mitigation.
Reliable performance metrics are necessary prerequisites to building large-scale end-to-end integrated workflows for collaborative scientific research, particularly within context of use-inspired decision making platforms with many concurrent users and when computing real-time and urgent results using large data. This work is a building block for the National Data Platform, which leverages multiple use-cases including the WIFIRE Data and Model Commons for wildfire behavior modeling and the EarthScope Consortium for collaborative geophysical research. This paper presents an artificial intelligence and machine learning (AI/ML) approach to performance assessment and optimization of scientific workflows. An associated early AI/ML framework spanning performance data collection, prediction and optimization is applied to wildfire science applications within the WIFIRE BurnPro3D (BP3D) platform for proactive fire management and mitigation.