A generic framework to analyse the spatiotemporal variations of water quality data on a catchment scale
(2019) In Environmental Modelling and Software 122.- 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
- Yang, Qinli ; Scholz, Miklas LU ; Shao, Junming ; Wang, Guoqing and Liu, Xiaofang
- organization
- publishing date
- 2019-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Cluster analysis, Dynamic time warping, Environmental data, Spatiotemporal analysis
- in
- Environmental Modelling and Software
- volume
- 122
- article number
- 104071
- 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
- 2022-04-25 04:24:20
@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}},
keywords = {{Cluster analysis; Dynamic time warping; Environmental data; Spatiotemporal analysis}},
language = {{eng}},
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}},
doi = {{10.1016/j.envsoft.2017.11.003}},
volume = {{122}},
year = {{2019}},
}