Prediction of fish location by combining fisheries data and sea bottom temperature forecasting
Ospici, M.; Sys, K.; Guegan-Marat, S. (2022). Prediction of fish location by combining fisheries data and sea bottom temperature forecasting, in: Sclaroff, S. et al. Image Analysis and Processing – ICIAP 2022. Lecture Notes in Computer Science, 13233: pp. 437-448. https://dx.doi.org/10.1007/978-3-031-06433-3_37
In: Sclaroff, S. et al. (2022). Image Analysis and Processing – ICIAP 2022. Lecture Notes in Computer Science, 13233. Springer: Cham. ISBN 978-3-031-06432-6; e-ISBN 978-3-031-06433-3. XIV, 495 pp. https://dx.doi.org/10.1007/978-3-031-06433-3, meer
In: Lecture Notes in Computer Science. Springer-Verlag: Heidelberg; Berlin. ISSN 0302-9743; e-ISSN 1611-3349, meer
|  |
| Beschikbaar in | Auteurs |
|
Documenttype: Congresbijdrage
|
| Trefwoord |
|
| Author keywords |
Computer vision; Machine learning; Spatiotemporal modelling; Fisheries; Remote sensing data |
| Auteurs | | Top |
- Ospici, M.
- Sys, K., meer
- Guegan-Marat, S.
|
|
|
| Abstract |
This paper combines fisheries dependent data and environmental data to be used in a machine learning pipeline to predict the spatio-temporal abundance of two species (plaice and sole) commonly caught by the Belgian fishery in the North Sea. By combining fisheries related features with environmental data, sea bottom temperature derived from remote sensing, a higher accuracy can be achieved. In a forecast setting, the predictive accuracy is further improved by predicting, using a recurrent deep neural network, the sea bottom temperature up to four days in advance instead of relying on the last previous temperature measurement. |
|
