Towards an Integrated Performance Framework for Fire Science and Management Workflows

• URL: http://arxiv.org/abs/2407.21231v1
• Date: Tue, 30 Jul 2024 22:37:25 GMT
• Title: Towards an Integrated Performance Framework for Fire Science and Management Workflows
• Authors: H. Ahmed, R. Shende, I. Perez, D. Crawl, S. Purawat, I. Altintas,
• Abstract summary: This paper presents an artificial intelligence and machine learning (AI/ML) approach to performance assessment and optimization. An associated early AI/ML framework spanning performance data collection, prediction and optimization is applied to wildfire science applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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