论文标题
通过组合渔业数据和海底温度预测来预测鱼位置
Prediction of fish location by combining fisheries data and sea bottom temperature forecasting
论文作者
论文摘要
本文结合了依赖渔业的数据和环境数据，用于在机器学习管道中使用，以预测北海的比利时渔业通常捕获的两种物种（Plaice and Sole）的时空丰度。通过将相关特征与环境数据相结合，从遥感中得出的海底温度可以实现更高的精度。在预测设置中，通过使用经常性的深神经网络预测预测的准确性可以进一步提高，即提前四天的海底温度，而不是依靠上一个先前的温度测量。
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.
