Abstract
Environmental processes are highly complex and their understanding involves the analysis of various quantitative and qualitative parameters (physical, chemical, geographical etc), which are more or less correlated. Appropriate environmental knowledge can deal with this complexity in a tractable way. Such knowledge is essential for solving particular environmental problems. Generating valuable environmental knowledge is a challenging research topic, especially for environmental data science, as efficient knowledge can lie behind data. Integrated environmental modelling uses a holistic view and can provide a possible better solution to environmental problems understanding. The paper presents a knowledge modelling framework for intelligent environmental decision support systems (IEDSS) by following such a holistic perspective. Thus, the proposed framework integrates an ontological approach and two data analysis approaches (data mining and Bayesian networks), which are applied for the generation of a knowledge base that is used by an IEDSS for decision making. The application of the framework is illustrated on three case studies from different environmental domains: (1) water (river resource management, river water pollution analysis), (2) air (air pollution analysis, ozone prediction), and (3) soil (soil pollution analysis).
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