Exploring the Use of Decision Tree Methodology in Hydrology Using Crowdsourced Data. (5th October 2020)

• Record Type: Journal Article
• Authors: Wu, Di; Del Rosario, Elizabeth A.; Lowry, Christopher
• Is Part Of: Journal of the American Water Resources Association. Volume 57:Number 2(2021)
• Page Start: 256
• Page End: 266
• Publication Date: 2020-10-05
• Subjects: rivers/streams -- public participation -- computational methods -- crowd hydrology -- citizen science -- crowdsourcing; Water-supply -- Periodicals; Hydrology -- Periodicals; Water resources development -- Periodicals; Water resources development -- Environmental aspects -- Periodicals; 333.9100973
• DOI: 10.1111/1752-1688.12882
• Languages: English
• ISSNs: 1093-474X

Abstract

Abstract: 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. Abstract : Research Impact Statement : The decision tree methodology has good performance on figuring out uncertainty in hydrological citizen science data and can be applied as a quality control tool for crowdsourcing dataset.