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Correlation of wave data from buoy networks

机译：浮标网络中波浪数据的相关性

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摘要

Missing oceanographic data resulting from an instrument that is damaged, malfunctioning, or otherwise non-operational can be an impediment to real-time applications of the data and also to statistical calculations pertaining to climatological studies. The problem of reconstructing lost information by correlating oceanographic data from multiple stations is explored through the use of artificial neural networks. A comprehensive simulation study was performed by developing six buoy networks (each containing several buoys) in locations with diverse geographical and wave properties: the northeastern part of the US, the Gulf of Mexico, and Prince William Sound (Alaska). The simulations demonstrate that the missing significant wave heights at the location of one buoy can, for the most part, be reliably reconstructed using artificial neural networks (ANNs) and data from other buoys. ANN model results in the northeastern part of the US tended to suffer from frequent under-prediction, while networks in the Gulf of Mexico and in Prince William Sound showed greater fidelity to measurements. The potential for redeploying some buoys to other locations and using the neural network model instead is examined.
机译：由仪器损坏，故障或其他原因导致的海洋数据丢失可能会阻碍数据的实时应用以及与气候研究有关的统计计算。通过使用人工神经网络探讨了通过关联来自多个站点的海洋数据来重建丢失信息的问题。通过在六个具有不同地理和波浪属性的地点开发六个浮标网络（每个浮标包含几个浮标）来进行全面的模拟研究：美国东北部，墨西哥湾和威廉王子湾（阿拉斯加）。仿真表明，在很大程度上，可以使用人工神经网络（ANN）和来自其他浮标的数据来可靠地重建一个浮标位置上丢失的重要波高。在美国东北部，人工神经网络模型的结果往往会遭受经常性的低估，而墨西哥湾和威廉王子湾的网络显示出对测量的更高保真度。研究了将某些浮标重新部署到其他位置并使用神经网络模型的可能性。

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