Towards an Integrated Performance Framework for Fire Science and Management Workflows

TL;DR

This paper proposes an AI/ML-based framework for assessing and optimizing performance in wildfire science workflows, supporting real-time decision making and large-scale collaborative research.

Contribution

It introduces an integrated AI/ML approach for performance evaluation and optimization tailored to wildfire management workflows within a large-scale data platform.

Findings

Effective performance prediction models developed

Workflow optimization improves response times

Framework supports real-time decision making

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.