OpFML: Pipeline for ML-based Operational Forecasting
By: Shahbaz Alvi , Giusy Fedele , Gabriele Accarino and more
Potential Business Impact:
Predicts wildfires better to keep us safe.
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.
Similar Papers
Do machine learning climate models work in changing climate dynamics?
Machine Learning (CS)
Helps predict rare, extreme weather events better.
Scaling Laws of Machine Learning for Optimal Power Flow
Machine Learning (CS)
Makes power grids smarter and faster.
A Multi-Criteria Automated MLOps Pipeline for Cost-Effective Cloud-Based Classifier Retraining in Response to Data Distribution Shifts
Machine Learning (CS)
Automates fixing computer brains when data changes.