We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

中文翻译：

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通过机器学习进行大气数据质量控制和误差校正的时空方法

我们提出了三种使用机器学习的质量控制（QC）技术，这些技术取决于用于训练的输入数据的类型。这些包括基于单个天气要素的时间序列的QC，基于与其他天气要素结合的时间序列的QC，以及使用时空特征的QC。我们对从七种IoT传感器获取的大气数据（例如温度）的每个天气要素进行了基于机器学习的质量控制，并对包含错误的数据应用了机器学习算法（例如支持向量回归）对它们进行有意义的估算。通过使用均方根误差（RMSE），我们评估了所提出技术的性能。结果，与仅使用一个天气要素进行的质量控制相比，与其他天气要素进行的质量控制的平均RMSE降低了0.14％。