Exploring the Use of Decision Tree Methodology in Hydrology Using Crowdsourced Data

Exploring the Use of Decision Tree Methodology in Hydrology Using Crowdsourced Data
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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

DOI:
10.1111/1752-1688.12882
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发表时间:
2020
影响因子:
2.4
通讯作者:
Wu, D.: Del
Wu, D.: Del
中科院分区:
环境科学与生态学4区
文献类型:
--
作者:
Wu, D.: Del

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为了填补对无资料河流的观测空白,众包分布式水文测量被认为是对观测数据网络的潜在补充。然而,公民科学数据具有不确定性,因为它们是由公众提供的。为了研究这种不确定性,决策树方法被应用到评估现有的公民科学数据的流阶段的基础上CrowdHydrology(CH)网络。开发了质量控制(QC)标记并应用于CH研究中心,将1级数据集(原始数据集)分为2级(标记数据集)和3级(处理数据集)。计算误差估计以确定公民科学数据的不确定性。结果表明,决策树可以为公民科学数据提供可靠的质量控制,并展示了如何在质量控制数据集中量化不确定性。
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.
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