{"refrec":{"BRefID":362210,"RR":"<b>Ospici, M.; Sys, K.; Guegan-Marat, S.</b> (2022). Prediction of fish location by combining fisheries data and sea bottom temperature forecasting, <b><i>in</i></b>: Sclaroff, S. <i>et al.</i> <i>Image Analysis and Processing – ICIAP 2022. Lecture Notes in Computer Science,</i> 13233: pp. 437-448. <a href=\"https://dx.doi.org/10.1007/978-3-031-06433-3_37\" target=\"_blank\">https://dx.doi.org/10.1007/978-3-031-06433-3_37</a>","BEntID":359926,"PublicFlag":1,"CheckedFlag":1,"wosflag":0,"vabbflag":1,"RefStringPartII":", <b><i>in</i></b>: Sclaroff, S. <i>et al.</i> <i>Image Analysis and Processing – ICIAP 2022. Lecture Notes in Computer Science,</i> 13233: pp. 437-448. <a href=\"https://dx.doi.org/10.1007/978-3-031-06433-3_37\" target=\"_blank\">https://dx.doi.org/10.1007/978-3-031-06433-3_37</a>","DocTypID":17,"DocType":"Book chapters","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Ospici, M.; Sys, K.; Guegan-Marat, S.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Ospici, M.; Sys, K.; Guegan-Marat, S.","Englishabstract":"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. 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