Abstract
The increasing deterioration of aquatic environments has attracted more attention to water quality monitoring techniques, with most researchers focusing on the acquisition and assessment of water quality data, but seldom on the discovery and tracing of pollution sources. In this study, a semantic-enhanced modeling method for ontology modeling and rules building is proposed, which can be used for river water quality monitoring and relevant data observation processing. The observational process ontology (OPO) method can describe the semantic properties of water resources and observation data. In addition, it can provide the semantic relevance among the different concepts involved in the observational process of water quality monitoring. A pollution alert can be achieved using the reasoning rules for the water quality monitoring stations. In this study, a case is made for the usability testing of the OPO models and reasoning rules by utilizing a water quality monitoring system. The system contributes to the water quality observational monitoring process and traces the source of pollutants using sensors, observation data, process models, and observation products that users can access in a timely manner.
Highlights
Water quality monitoring using in-situ environmental sensors presents many challenges for data discovery, for the warning and querying of water pollution
Study, an anontology ontologyfor forwater water quality monitoring is used for water pollution
In this quality monitoring thatthat is used for water pollution alerts alerts was proposed
Summary
Water quality monitoring using in-situ environmental sensors presents many challenges for data discovery, for the warning and querying of water pollution. The OWL is an ontological language for the semantic web with formally defined meanings that provide the description of classes, properties, individuals, and data values, and these are stored as Semantic These tools provide two promising areas for water quality monitoring research: (a) the semantic modeling of water observation data and (b) how to use the semantic information of observation data in a water quality monitoring system. The objective is to is to achieve a semantic modeling of the observational process in Sensor Web by integrating water quality monitoring and alerts. To achieve this goal, an observational process ontology modeling method is described, which consists of observation acquisition, observation data, observation processing, and observation products.
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