TY - GEN
T1 - Spatial dynamics of the global water quality analysis system with semantic-ordering functions
AU - Veesommai, Chalisa
AU - Kiyoki, Yasushi
N1 - Funding Information:
This work has been supported in the part by MEXT Grant-in-Aid for the Program for Leading Graduate School Program (GESL), the Taikichiro Mori Memorial Research Fund, and Multimedia Database Laboratory (MDBL), Graduate School of Media and Governance, Keio University.
Publisher Copyright:
© 2018 The authors and IOS Press. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Global water-quality analysis system
KW - Multidimensional semantic spaces
KW - Semantic-analysis
KW - Sensing Processing Actuation (SPA) process
KW - Spatial dynamics
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U2 - 10.3233/978-1-61499-834-1-149
DO - 10.3233/978-1-61499-834-1-149
M3 - Conference contribution
AN - SCOPUS:85063392226
T3 - Frontiers in Artificial Intelligence and Applications
SP - 149
EP - 163
BT - Information Modelling and Knowledge Bases XXIX
A2 - Chawakitchareon, Petchporn
A2 - Kiyoki, Yasushi
A2 - Hansuebsai, Aran
A2 - Koopipat, Chawan
A2 - Sornlertlamvanich, Virach
A2 - Yoshida, Naofumi
A2 - Jaakkola, Hannu
A2 - Thalheim, Bernhard
PB - IOS Press
T2 - 27th International Conference on Information Modelling and Knowledge Bases, EJC 2017
Y2 - 5 June 2017 through 9 June 2017
ER -