To form precipitation datasets that are accurate and, at the same time, have
high spatial densities, data from satellites and gauges are often merged in the
literature. However, uncertainty estimates for the data acquired in this manner
are scarcely provided, although the importance of uncertainty quantification in
predictive modelling is widely recognized. Furthermore, the benefits that
machine learning can bring to the task of providing such estimates have not
been broadly realized and properly explored through benchmark experiments. The
present study aims at filling in this specific gap by conducting the first
benchmark tests on the topic. On a large dataset that comprises 15-year-long
monthly data spanning across the contiguous United States, we extensively
compared six learners that are, by their construction, appropriate for
predictive uncertainty quantification. These are the quantile regression (QR),
quantile regression forests (QRF), generalized random forests (GRF), gradient
boosting machines (GBM), light gradient boosting machines (LightGBM) and
quantile regression neural networks (QRNN). The comparison referred to the
competence of the learners in issuing predictive quantiles at nine levels that
facilitate a good approximation of the entire predictive probability
distribution, and was primarily based on the quantile and continuous ranked
probability skill scores. Three types of predictor variables (i.e., satellite
precipitation variables, distances between a point of interest and satellite
grid points, and elevation at a point of interest) were used in the comparison
and were additionally compared with each other. This additional comparison was
based on the explainable machine learning concept of feature importance. The
results suggest that the order from the best to the worst of the learners for
the task investigated is the following: LightGBM, QRF, GRF, GBM, QRNN and QR...

通过对具有 15 年长期月度跨越美国的大型数据集进行广泛比较，本研究旨在首次进行基于机器学习的不确定性量化任务的基准测试，结论表明适用于此任务的学习器的能力排名分别为：LightGBM，QRF，GRF，GBM，QRNN 和 QR。