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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.11046 |
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| _version_ | 1866914258470567936 |
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| author | Alvi, Shahbaz Fedele, Giusy Accarino, Gabriele Epicoco, Italo Manco, Ilenia Schiano, Pasquale |
| author_facet | Alvi, Shahbaz Fedele, Giusy Accarino, Gabriele Epicoco, Italo Manco, Ilenia Schiano, Pasquale |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11046 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OpFML: Pipeline for ML-based Operational Forecasting Alvi, Shahbaz Fedele, Giusy Accarino, Gabriele Epicoco, Italo Manco, Ilenia Schiano, Pasquale Machine Learning 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. |
| title | OpFML: Pipeline for ML-based Operational Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.11046 |