# Towards an Integrated Performance Framework for Fire Science and Management Workflows

arXiv: https://arxiv.org/abs/2407.21231 · HTML: https://mlsanity.com/paper/2407.21231/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [BanditWare: A Contextual Bandit-based Framework for Hardware Prediction](https://mlsanity.com/paper/2506.13730/) (2025)

## Abstract (arXiv)

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.