Prediction of fish location by combining fisheries data and sea bottom temperature forecasting

TL;DR

This study enhances fish location prediction by integrating fisheries data with forecasted sea bottom temperatures using machine learning, notably improving accuracy for Belgian North Sea species.

Contribution

It introduces a novel approach combining fisheries data with forecasted environmental variables, specifically sea bottom temperature, in a deep learning pipeline for fish abundance prediction.

Findings

Higher predictive accuracy with combined data.

Forecasting sea bottom temperature improves predictions.

Recurrent neural networks effectively model temporal dynamics.

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.