To fill the observations gap on ungauged streams, crowdsourced distributed hydrologic measurements were considered as a potential supplement for observational data networks. However, citizen science data come with uncertainty as they are provided by the general public. In order to investigate this uncertainty, a decision tree methodology was applied to evaluate existing citizen science data of stream stage based on the CrowdHydrology (CH) network. Quality control (QC) flags were developed and applied to CH sites, dividing Level 1 dataset (raw dataset) into Level 2 (flagged dataset) and Level 3 (processed dataset). Error estimates were calculated to determine uncertainty in the citizen science data. The results indicate that the decision tree could provide reliable QC for citizen science data and demonstrate how uncertainty can be quantified in the QC datasets.

中文翻译：

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利用众包数据探索决策树方法在水文学中的应用

为了填补未加水流的观测缺口，将众包的分布式水文测量结果视为观测数据网络的潜在补充。但是，公民科学数据是由公众提供的，因此具有不确定性。为了调查这种不确定性，基于CrowdHydrology（CH）网络，采用决策树方法评估现有的河流阶段公民科学数据。开发了质量控制（QC）标志并将其应用于CH站点，将1级数据集（原始数据集）分为2级（标记数据集）和3级（已处理数据集）。计算误差估计以确定公民科学数据中的不确定性。