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Delivering environmental information for decision support in dynamic conditions

  • Kotovirta, Ville
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This thesis studied improvements in the timely delivery of relevant near real-time environmental information to support decision making in dynamic environments. Especially, the focus was on orchestration of the data processing tasks, presentation of the information to end-users, and data collection from the field where the users are operating. It was found that three system design principles can be used to improve the information delivery: 1) organizing the synergies in data access and information processing as a component called Data operator, 2) including automatic analyses of the situation to support the interpretation of the information, and 3) harnessing of end-users and end-user devices as opportunistic and participatory sensors to collect data from the local conditions in order to complement other data sources. The research was conducted by studying two application cases: ice navigation and water quality monitoring. In the ice navigation case, we developed a system to deliver in-situ and remote sensing data as well as forecasts by computational models about the meteorological, oceanographic and ice conditions to ice-going ships, a route optimisation method to support the decision making and information presentation, and a method for using ships and ship radars as a sensor network. In the water quality monitoring case, citizens were harnessed as observers of water turbidity and the algae situation in order to complement other data sources, and citizen observations were compared with expert observations. Open data and open interfaces are important elements for accessing data, but they are not adequate to guarantee the optimal use of environmental data in near real-time applications. The whole processing chain from data sources to end-user awareness should be considered in order to take full advantage of the data. It is concluded that the three design principles are not limited to the application cases of this study, but are applicable to other domains of environmental monitoring as well, for example air quality, disaster and built environment monitoring. The amount of environmental data is growing exponentially, and new methods are needed to include these data in decision making in society.

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