OpFML: Pipeline for ML-based Operational Forecasting
This work addresses wildfire danger assessment for climate scientists and forecasters, but it appears incremental as it focuses on a pipeline rather than novel methods or significant gains.
The authors tackled the problem of operational forecasting in climate and earth sciences by developing OpFML, a configurable pipeline for deploying machine learning models, and demonstrated it for daily Fire Danger Index forecasting, though no concrete numbers on performance improvements are provided.
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.