基于海洋遥感与渔船轨迹数据的多任务鱼情预测

Multi-task Fishing Condition Prediction Based on Ocean Sensing and Vessel Trajectories Data

渔情预测,即预测未来鱼群的位置和该区域内鱼量的丰富度.通过了解未来的渔情信息,管理者可以制定行之有效的策略,对渔民来说也可以节省捕鱼过程中的资源消耗.本文从海洋环境遥感数据与AIS渔船轨迹数据着手,分析挖掘鱼群的分布状况,进而对未来的渔情进行预测.根据作业方式的不同,渔船可以分为多种类型,如围网、刺网、拖网、张网等,针对装备不同渔具的渔船预测出未来作业的区域,进行精细化管理具有重要意义.以往的单任务学习能够对各渔具实现单独预测,但不能捕获各种渔具之间的相互影响.为此,本文提出了一种基于海洋遥感数据与AIS渔船轨迹数据的时空神经网络的多任务预测方法,在对每种渔具单独预测的同时捕获各渔具之间的相互影响.同时,将诸如海洋温度、盐度等环境遥感数据嵌入到模型中,进一步提高了预测的准确度.在浙江海域的AIS渔船轨迹数据集上进行了实验,结果证明了该方法相对于经典和最新的基于海洋遥感与AIS轨迹预测鱼群分布状况的优越性.

The prediction of fishing conditions is to predict the locations of fish schools and the abundance of fish in those areas. With knowledge of future fishing conditions, managers can formulate effective strategies and fishermen can cut down their resource consumption in the fishing process. This study starts with the remote sensing data of the marine environment and automatic identification system(AIS) fishing vessel trajectory data, analyzes the distribution of fish schools, and predicts future fishing conditions. According to different operation methods, fishing vessels can be divided into many types, such as purse seine, gillnet, trawl, and stow net types. It is of great significance to predict the future operation areas of fishing vessels equipped with different fishing gears and carry out fine management. The traditional single-task learning can achieve individual predictions for each fishing gear, but it cannot capture the interaction of various fishing gears. Therefore, this study proposes a multi-task prediction method based on a spatiotemporal neural network of ocean remote sensing data and AIS fishing vessel trajectory data. This method can capture the interaction of the fishing gears in addition to conducting separate predictions for each fishing gear. The prediction accuracy is further improved by embedding environmental remote sensing data such as ocean temperature and salinity into the model.Experiments are conducted on the data set of AIS fishing vessel trajectories in Zhejiang sea area, China, and the results prove the superiority of this method to the classical method and the latest one based on ocean remote sensing and AIS trajectory inpredicting the distribution of fish schools.