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首页> 外文期刊>The International Hydrographic Review >Sequential Sea-ice Concentration Prediction for Marine Operations in Ice-Infested Waters
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Sequential Sea-ice Concentration Prediction for Marine Operations in Ice-Infested Waters

机译：冰灾海域海洋作业的顺序海冰浓度预测

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摘要

Marine operations in ice-infested waters require reliable and timely information about the sea ice conditions. The Canadian Ice Service produces and distributes the ice information to mariners operating in Canadian waters, mainly in the form of daily ice charts. Unfortunately, due to the time difference between the production and the use of the ice charts, the ice information is always out of date, which endangers the safety of marine operations. To efficiently overcome this problem, a reliable model for predicting the sea ice concentration over time is developed. Examining the ice charts of the Gulf of St. Lawrence during the period 1987 to 1998 showed that the sea ice conditions change according to a regular pattern to some extent. Therefore, a neural network function approximation system could model, and hence predict, these changes efficiently when trained, using multiple-year ice concentration readings. Initially, the training was done in the batch mode. However, this was found inefficient when abrupt changes in the values of the ice concentration were encountered. Therefore, a sequential model, which uses the modular neural network structure, was developed. In addition to overcoming the drawbacks of the batch method, the sequential model is more suitable for real-time applications.
机译：在充满冰的水域中进行海上作业需要有关海冰状况的可靠和及时的信息。加拿大冰服务局主要以每日冰图的形式生产冰信息并将其分发给在加拿大水域工作的水手。不幸的是，由于冰图的生产和使用之间存在时间差异，因此冰信息始终是过时的，从而危及海上作业的安全。为了有效地克服这个问题，开发了一种可靠的模型来预测随时间的海冰浓度。查看1987年至1998年期间圣劳伦斯湾的冰图，可以发现海冰的状况在一定程度上按照规则规律变化。因此，神经网络功能近似系统可以使用多年的冰浓度读数进行建模，从而在训练时有效地预测这些变化。最初，培训是在批处理模式下完成的。但是，当遇到冰浓度值的突然变化时，发现效率低下。因此，开发了使用模块化神经网络结构的顺序模型。除了克服批处理方法的缺点，顺序模型更适合于实时应用。

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