Predicting seasonal and hydro-meteorological impact in environmental variables modelling via Kalman filtering
This study focuses on the potential improvement of environmental variables modelling by using linear state-space models, as an improvement of the linear regression model, and by incorporating a constructed hydro-meteorological covariate. The Kalman filter predic- tors allow to obtain accurate predic...
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| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2012
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| Texto completo: | http://hdl.handle.net/10773/9191 |
| País: | Portugal |
| Oai: | oai:ria.ua.pt:10773/9191 |
| Resumo: | This study focuses on the potential improvement of environmental variables modelling by using linear state-space models, as an improvement of the linear regression model, and by incorporating a constructed hydro-meteorological covariate. The Kalman filter predic- tors allow to obtain accurate predictions of calibration factors for both seasonal and hydro-meteorological components. This methodology can be used to analyze the water quality behaviour by minimizing the effect of the hydrological conditions. This idea is illustrated based on a rather extended data set relative to the River Ave basin (Portugal) that consists mainly of monthly measurements of dissolved oxygen concentration in a network of water quality monitoring sites. The hydro-meteorological factor is constructed for each monitoring site based on monthly precipitation estimates obtained by means of a rain gauge network associated with stochastic interpolation (kriging). A linear state-space model is fitted for each homogeneous group (obtained by clustering techniques) of water monitoring sites. The adjustment of linear state-space models is performed by using distribution-free estimators developed in a separate section. |
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