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| Renosh, Pannimpullath R.; Jourdin, Frederic; Charantonis, Anastase A.; Yala, Khalil; Rivier, Aurelie; Badran, Fouad; Thiria, Sylvie; Guillou, Nicolas; Leckler, Fabien; Gohin, Francis; Garlan, Thierry. |
| Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden... |
| Tipo: Text |
Palavras-chave: Suspended particulate inorganic matter; Self-organizing maps; Hidden Markov Model; Machine learning; English Channel; ROMS. |
| Ano: 2017 |
URL: http://archimer.ifremer.fr/doc/00415/52653/53511.pdf |
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| Vermard, Youen; Rivot, Etienne; Mahevas, Stephanie; Marchal, Paul; Gascuel, Didier. |
| Recent advances in technologies have lead to a vast influx of data on movements, based on discrete recorded position of animals or fishing boats, opening new horizons for future analyses. However, most of the potential interest of tracking data depends on the ability to develop suitable modelling strategies to analyze trajectories from discrete recorded positions. A serious modelling challenge is to infer the evolution of the true position and the associated spatio-temporal distribution of behavioural states using discrete, error-prone and incomplete observations. In this paper, a Bayesian Hierarchical Model (HBM) using Hidden Markov Process (HMP) is proposed as a template for analyzing fishing boats trajectories based on data available from... |
| Tipo: Text |
Palavras-chave: Bayesian Hierarchical Models; Hidden Markov Model; State-space model; VMS; Fleet behaviour; Fishing effort. |
| Ano: 2010 |
URL: http://archimer.ifremer.fr/doc/00009/11993/9342.pdf |
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