Arctic Sea Ice Extent Forecasting Using Support Vector Regression
Arctic Sea Ice Extent Forecasting Using Support Vector Regression
复制标题
使用支持向量回归预测北极海冰范围
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
P. Tarantino
中科院分区：
文献类型：
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作者：
T. Reid;P. Tarantino
夏季北极海冰最小范围长期以来一直被用来衡量气候变化，近年来报告的最低值创历史新低。了解北极海冰范围的动态对于了解与这种变化相关的时间尺度至关重要。通常采用复杂的全球气候模型来深入了解北极海冰的未来，然而，这些模型的计算成本通常非常高，而且结果往往存在争议。在这里，我们使用遥感卫星的历史数据以及机器学习算法来预测北极海冰范围。支持向量回归用于学习动态模型来表示该系统。验证结果表明该方法能够成功预测北极海冰覆盖的季节性和长期趋势。
The summer minimum Arctic sea ice extent has long been used as a measure of climate change, with record lows being reported in recent years. Understanding the dynamics of the Arctic sea ice extent is of utmost importance in understanding the timescales associated with this change. Complex global climate models are typically employed to gain insights about the future of Arctic sea ice, however, these models are typically very computationally expensive to solve and the results are often controversial. Here, we use historical data from remote sensing satellites along with machine learning algorithms in the forecasting of the Arctic sea ice extent. Support Vector Regression is employed in the learning of a dynamic model to represent this system. Validation results demonstrate the ability of the method to successfully forecast both the seasonal and long-term trends in Arctic sea ice coverage.