基于人工神经网络的北极海域大气能见度格点数据生成技术

GRIDDED DATA GENERATING TECHNOLOGY OF ATMOSPHERIC VISIBILITY IN THE ARCTIC SEA BASED ON ARTIFICIAL NEURAL NETWORK

随着北极航线的逐渐开通,北极航道的通航风险评估工作得到更多的关注。针对当前北极航道风评工作中存在的能见度数据稀缺等问题,提出一种基于能见度推理结果与观测数据相结合的能见度格点数据生成技术。通过确定能见度影响因子、生成样本数据集、人工神经网络技术和贝叶斯推理技术的拟合效果对比、订正推理所得能见度数据等流程,构建了基于人工神经网络推理结果和观测数据相结合的能见度格点数据生成模型,并将该推理结果与传统插值结果进行对比。结果表明,基于该模型生成的能见度格点数据的准确度高于插值结果,且结果更为科学,能够为我国的极地航道风评工作提供重要参考。

With the gradual opening of the Arctic Passage,attention is increasing on the assessment of risk associated with sailing across the Arctic.In addressing the problem of the lack of visibility data in the Arctic region,this study proposed a technique for generating gridded visibility data by fusing reasoning results and measured visibility data.Through determination of the factors influencing visibility,the generation of sample data sets,comparison of the fitting effects between an artificial neural network and a Bayesian network and revision of the inferred results,an inference model of gridded visibility data was constructed based on an artificial neural network and the reasoning and interpolation results of visibility were compared.In comparison with the interpolation technique,the results showed the inference model produced values that were more accurate.Therefore,the developed technique could provide important reference material for risk assessment studies of the Arctic Passage.