Prediction of fish location by combining fisheries data and sea bottom temperature forecasting
This work addresses spatio-temporal prediction for fisheries management, but it is incremental as it builds on existing methods by adding temperature forecasting.
The paper tackles predicting fish abundance for plaice and sole in the North Sea by combining fisheries and environmental data, achieving higher accuracy with a machine learning pipeline that includes forecasting sea bottom temperature up to four days in advance using a recurrent deep neural network.
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.