An informatics approach for smart evaluation of water quality related ecosystem services

Yan, Weigang; Hutchins, Mike; Loiselle, Steven; Hall, Charlotte. 2016 An informatics approach for smart evaluation of water quality related ecosystem services. Annals of Data Science, 3 (3). 251-264.

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Understanding the relationship between water quality and ecosystem services valuation requires a broad range of approaches and methods from the domains of environmental science, ecology, physics and mathematics. The fundamental challenge is to decode the association between ‘ecosystem services geography’ with water quality distribution in time and in space. This demands the acquisition and integration of vast amounts of data from various domains in many formats and types. Here we present our system development concept to support the research in this field. We outline a technological approach that harnesses the power of data with scientific analytics and technology advancement in the evolution of a data ecosystem to evaluate water quality. The framework integrates the mobile applications and web technology into citizen science, environmental simulation and visualization. We describe a schematic design that links water quality monitoring and technical advances via data collection by citizen scientists and professionals to support ecosystem services evaluation. These data were synthesized into big data analytics through a Bayesian belief network to assess ecosystem services related to water quality. Finally, the paper identifies technical barriers and opportunities, in respect of big data ecosystem, for valuating water quality in ecosystem services assessment.

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Acreman
Rees (from October 2014)
ISSN: 2198-5804
Additional Keywords: water quality, data ecosystem, citizen science, big data, informatics, Bayesian belief networks
NORA Subject Terms: Ecology and Environment
Computer Science
Data and Information
Date made live: 05 Dec 2016 16:52 +0 (UTC)

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