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Exploring the Use of Decision Tree Methodology in Hydrology Using Crowdsourced Data
Author(s) -
Wu Di,
Del Rosario Elizabeth A.,
Lowry Christopher
Publication year - 2021
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12882
Subject(s) - citizen science , decision tree , raw data , computer science , tree (set theory) , data mining , data science , hydrology (agriculture) , mathematics , engineering , mathematical analysis , botany , geotechnical engineering , biology , programming language
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.