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A generic framework to analyse the spatiotemporal variations of water quality data on a catchment scale

Yang, Qinli; Scholz, Miklas LU ; Shao, Junming; Wang, Guoqing and Liu, Xiaofang (2017) In Environmental Modelling and Software
Abstract

Most spatiotemporal studies treat spatial and temporal analysis separately. However, spatial and temporal changes occur simultaneously and are correlated. In this study, we propose a generic framework to simultaneously analyse the spatial and temporal variations of water quality on a catchment scale. Specifically, we analyse the heterogeneity of temporal evolution of water quality data among different sampling sites, and the heterogeneity of spatial distribution of water quality data over different sampling times, respectively, by integrating the techniques of normalized mutual information, dynamic time wrapping and cluster analysis. To bring deep insight into the spatiotemporal variations, inter-change and intra-change are further... (More)

Most spatiotemporal studies treat spatial and temporal analysis separately. However, spatial and temporal changes occur simultaneously and are correlated. In this study, we propose a generic framework to simultaneously analyse the spatial and temporal variations of water quality on a catchment scale. Specifically, we analyse the heterogeneity of temporal evolution of water quality data among different sampling sites, and the heterogeneity of spatial distribution of water quality data over different sampling times, respectively, by integrating the techniques of normalized mutual information, dynamic time wrapping and cluster analysis. To bring deep insight into the spatiotemporal variations, inter-change and intra-change are further defined and distinguished, respectively. Taking the Fuxi River catchment as a case study, results indicate that the proposed framework is intuitive and efficient. Beyond this, the generic framework can be expanded for other catchments and various environmental data.

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author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Cluster analysis, Dynamic time warping, Environmental data, Spatiotemporal analysis
in
Environmental Modelling and Software
publisher
Elsevier
external identifiers
  • scopus:85034786742
ISSN
1364-8152
DOI
10.1016/j.envsoft.2017.11.003
language
English
LU publication?
yes
id
3d90236f-b8a3-439e-9ede-8f02d3b5bf34
date added to LUP
2017-12-11 14:24:27
date last changed
2018-01-07 12:28:09
@article{3d90236f-b8a3-439e-9ede-8f02d3b5bf34,
  abstract     = {<p>Most spatiotemporal studies treat spatial and temporal analysis separately. However, spatial and temporal changes occur simultaneously and are correlated. In this study, we propose a generic framework to simultaneously analyse the spatial and temporal variations of water quality on a catchment scale. Specifically, we analyse the heterogeneity of temporal evolution of water quality data among different sampling sites, and the heterogeneity of spatial distribution of water quality data over different sampling times, respectively, by integrating the techniques of normalized mutual information, dynamic time wrapping and cluster analysis. To bring deep insight into the spatiotemporal variations, inter-change and intra-change are further defined and distinguished, respectively. Taking the Fuxi River catchment as a case study, results indicate that the proposed framework is intuitive and efficient. Beyond this, the generic framework can be expanded for other catchments and various environmental data.</p>},
  author       = {Yang, Qinli and Scholz, Miklas and Shao, Junming and Wang, Guoqing and Liu, Xiaofang},
  issn         = {1364-8152},
  keyword      = {Cluster analysis,Dynamic time warping,Environmental data,Spatiotemporal analysis},
  language     = {eng},
  month        = {11},
  publisher    = {Elsevier},
  series       = {Environmental Modelling and Software},
  title        = {A generic framework to analyse the spatiotemporal variations of water quality data on a catchment scale},
  url          = {http://dx.doi.org/10.1016/j.envsoft.2017.11.003},
  year         = {2017},
}