Early adaptive evaluation scheme for data-driven calibration in forest fire spread prediction
Forest fires severally affect many ecosystems every year, leading to large environmental damages, casualties and economic losses. Established and emerging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more accurate prediction results and efficient use...
| Autores: | , , , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:324473 |
| Acceso en línea: | https://ddd.uab.cat/record/324473 https://dx.doi.org/urn:doi:10.1007/978-3-030-50433-5_2 |
| Access Level: | acceso abierto |
| Palabra clave: | Data driven prediction Data uncertainty Forest fires Urgent computing |
| Sumario: | Forest fires severally affect many ecosystems every year, leading to large environmental damages, casualties and economic losses. Established and emerging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more accurate prediction results and efficient use of resources in fire fighting. Natural hazards simulations need to deal with data input uncertainty and their impact on prediction results, usually resorting to compute-intensive calibration techniques. In this paper, we propose a new evaluation technique capable of reducing the overall calibration time by 60% when compared to the current data-driven approaches. This is achieved by means of the proposed adaptive evaluation technique based on a periodic monitoring of the fire spread prediction error. |
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