The rapid temporal and spatial changes of human population and technology development have resulted in the expansion of agriculture, industrial activity, and deforestation. Those massive expansions affect water resources, especially river. Since the river is the main resource of water in human life, it is crucial to analyze the river water-quality in order to detect the water contamination. In this paper, we present an automatic system for water-quality analysis using several databases and different contexts in dynamic sub-space selection contexts. This system obtains information resources by transforming the sensor-value information to language information. This system aims to monitor, analyze and evaluate the Global Water-quality by using Semantic-ordering functions both in single and multiple parameters. Semantic-ordering is used for spatial-dynamics environmental changes in multiple contexts (aquatic life, agricultural, drinking, fish, industrial usage, and irrigation context). As for the experimental study, four places have been selected as study areas; (1) Hawaii (USA), (2) Pori, (Finland), (3) Riga (Latvia), and (4) Vientiane (Laos). The data resource was acquired from March to September 2016. The result shows that this system is able to analyze and identify the ordering of the different water-quality on different places in the global point of view level and to present the global-scale ranking of water quality.
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