OpFML: Pipeline for ML-based Operational Forecasting

2026-01-16

AI Summary

Traditional wildfire risk assessment methods often overestimate risk and lack efficient, periodically deployable data-driven forecasting systems. To address this gap, this work proposes OpFML—the first general-purpose pipeline framework designed specifically for operational machine learning-based forecasting. OpFML supports configuration-driven workflows, automated scheduling, and end-to-end deployment, offering high flexibility and reusability. The framework has been successfully applied to daily fire danger index prediction, demonstrating its feasibility and effectiveness in real-world Earth science operational scenarios.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Machine learning is finding its application in a multitude of areas in science and research, and Climate and Earth Sciences is no exception to this trend. Operational forecasting systems based on data-driven approaches and machine learning methods deploy models for periodic forecasting. Wildfire danger assessment using machine learning has garnered significant interest in the last decade, as conventional methods often overestimate the risk of wildfires. In this work, we present the code OpFML: Operational Forecasting with Machine Learning. OpFML is a configurable and adaptable pipeline that can be utilized to serve a machine learning model for periodic forecasting. We further demonstrate the capabilities of the pipeline through its application to daily Fire Danger Index forecasting and outline its various features.

Problem

• wildfire danger forecasting
• operational forecasting
• machine learning
• Fire Danger Index

Innovation

• Operational forecasting
• Machine learning pipeline
• Wildfire danger assessment
• Fire Danger Index
• Configurable framework