A generic framework to analyse the spatiotemporal variations of water quality data on a catchment scale. (December 2019)

• Record Type: Journal Article
• Authors: Yang, Qinli; Scholz, Miklas; Shao, Junming; Wang, Guoqing; Liu, Xiaofang
• Is Part Of: Environmental modelling & software. Volume 122(2019)
• Publication Date: 2019-12
• Subjects: Spatiotemporal analysis -- Environmental data -- Cluster analysis -- Dynamic time warping; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.11.003
• Languages: English
• ISSNs: 1364-8152

Abstract

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 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. Graphical abstract: Image 1 Highlights: Propose a generic framework for spatiotemporal variation analysis of water quality. Inter- and intra-change of spatiotemporal variations of water quality were studied. Integrate data mining techniques to solve environmental problems. The proposed framework has the potential to be applied to other environmental data.