Locating fuel breaks to minimise the risk of impact of wild fire
In order to respond the question “Where to locate fuel breaks?”, a peculiar location model is presented involving stochastic mixed integer nonlinear optimization, Bayesian networks and directional statistic inference. From a first simple approximation to the large model, will be shown what motivates...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/19397 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/19397 |
| Access Level: | acceso abierto |
| Palabra clave: | 519.856 519.853 Stochastic programming Mixed integer programming Nonlinear programming Bayesian inference Estadística matemática (Matemáticas) Investigación operativa (Matemáticas) 1209 Estadística 1207 Investigación Operativa |
| Sumario: | In order to respond the question “Where to locate fuel breaks?”, a peculiar location model is presented involving stochastic mixed integer nonlinear optimization, Bayesian networks and directional statistic inference. From a first simple approximation to the large model, will be shown what motivates follow models and its complexity incorporated. Also, a case study with real data about Corsica region is presented. |
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