Bayesian networks for probabilistic weather prediction

Autor:	Cofiño González, Antonio Santiago, Cano Trueba, Rafael, Sordo, Carmen María, Gutiérrez Llorente, José Manuel
Rok vydání:	2002
Předmět:	Rainfall forecast Probabilistic weather prediction Bayesian Networks Time series prediction Acyclic graph
Zdroj:	ARCIMIS. Archivo Climatológico y Meteorológico Institucional (AEMET) Agencia Estatal de Meteorología (AEMET)
Popis:	Ponencia presentada en: 15th European Conference on Artificial Intelligence celebrada los días 21-26 de julio en Lyon, Francia Several standard approaches have been introduced for meteorological time series prediction (analog techniques, neural networks, etc.). However, when dealing with multivariate spatially distributed time series (e.g., a network of meteorological stations over the Iberian peninsula) the above methods do not consider all the available information (they consider special independency assumptions to simplify the model). In this work, we introduce Bayesian Networks (BNs) in this framework to model the spatial and temporal dependencies among the different stations using a directed acyclic graph.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::243dd8459e5397874fec97faf030b24b http://hdl.handle.net/20.500.11765/3809 Zobrazit plný text záznamu