Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms
Explanation and Probabilistic Prediction of Hydrological Signatures with Statistical Boosting Algorithms
复制标题
用统计增强算法对水文特征进行解释和概率预测
作者：
Hristos Tyralis;Georgia Papacharalampous;A. Langousis;S. Papalexiou
水文特征，即水流时间序列的统计特征，用于表征一个地区的水文特征。一个相关问题是使用从未测量和测量区域的遥感测量获得的属性以及来自测量区域的估计水文特征来预测未测量区域的水文特征。相关框架被表述为回归问题，其中属性是预测变量，水文特征是因变量。在这里，我们的目标是在回归设置中使用统计增强来提供水文特征的概率预测。我们使用美国本土 667 个流域的 28 个属性预测 12 个水文特征。我们使用分位数分数对概率预测进行正式评估。我们还利用派生模型的可解释性方面的统计增强特性。结果表明，与同时使用线性模型和树桩（即一级决策树）的更灵活的提升模型相比，使用线性模型作为基础学习器的分位数级别 2.5% 和 97.5% 的概率预测表现出更好的性能。相反，当用于点预测时，同时使用线性模型和树桩的 boosting 模型比使用线性模型的 boosting 表现更好。此外，研究表明，气候指数和地形特征是预测水文特征的最重要属性。
Hydrological signatures, i.e., statistical features of streamflow time series, are used to characterize the hydrology of a region. A relevant problem is the prediction of hydrological signatures in ungauged regions using the attributes obtained from remote sensing measurements at ungauged and gauged regions together with estimated hydrological signatures from gauged regions. The relevant framework is formulated as a regression problem, where the attributes are the predictor variables and the hydrological signatures are the dependent variables. Here we aim to provide probabilistic predictions of hydrological signatures using statistical boosting in a regression setting. We predict 12 hydrological signatures using 28 attributes in 667 basins in the contiguous US. We provide formal assessment of probabilistic predictions using quantile scores. We also exploit the statistical boosting properties with respect to the interpretability of derived models. It is shown that probabilistic predictions at quantile levels 2.5% and 97.5% using linear models as base learners exhibit better performance compared to more flexible boosting models that use both linear models and stumps (i.e., one-level decision trees). On the contrary, boosting models that use both linear models and stumps perform better than boosting with linear models when used for point predictions. Moreover, it is shown that climatic indices and topographic characteristics are the most important attributes for predicting hydrological signatures.