Optimization of deep learning precipitation models using categorical binary metrics
This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection or false alarm rate are popular metrics used in the verification of precipitation models. How...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Basque Center for Applied Mathematics (BCAM) |
| Repositorio: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/1106 |
| Acceso en línea: | http://hdl.handle.net/20.500.11824/1106 https://doi.org/10.1002/qj.828. |
| Access Level: | acceso abierto |
| Palabra clave: | machine learning optimization deep learning precipitations binary metrics |
| Sumario: | This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection or false alarm rate are popular metrics used in the verification of precipitation models. However, machine learning models trained using gradient descent cannot be optimized based on these metrics, as they are not differentiable. We propose an alternative formulation for these categorical indices that are differentiable and we demonstrate how they can be used to optimize the skill of precipitation neural network models defined as a multi-objective optimization problem. To our knowledge, this is the first proposal of a methodology for optimizing weather neural network models based on categorical indices. |
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