• Journal of Information Processing Systems
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• v.9 no.1
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• pp.1-30
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• 2013

The capitalization of know-how, knowledge management, and the control of the constantly growing information mass has become the new strategic challenge for organizations that aim to capture the entire wealth of knowledge (tacit and explicit). Thus, knowledge mapping is a means of (cognitive) navigation to access the resources of the strategic heritage knowledge of an organization. In this paper, we present a new mapping approach based on the Boolean modeling of critical domain knowledge and on the use of different data sources via the data mining technique in order to improve the process of acquiring knowledge explicitly. To evaluate our approach, we have initiated a process of mapping that is guided by machine learning that is artificially operated in the following two stages: data mining and automatic mapping. Data mining is be initially run from an induction of Boolean case studies (explicit). The mapping rules are then used to automatically improve the Boolean model of the mapping of critical knowledge.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.621-635
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• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.