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learning content management system, personalized learning, ontology, smart learning content  \begin{abstract}  Decision making in river floodplain \keywords{learning content  managementis a complex process that involves many stakeholders and experts. Since stakeholders and experts often pursue mutually exclusive objectives and are often geographically distributed, decision making process takes a long time and not as optimal as it should be. Use of intelligent decision support system (IDSS) allows to decrease the duration of decision making process and to improve the quality and efficiency of decisions. In this paper we present the knowledge-based system for intelligent support of decision making in river floodplain management. This system integrates the case based reasoning (CBR), qualitative reasoning (QR) and ontological knowledge base. Proposed knowledge representation model is formally represented by the OWL DL ontology. For this model we give the descriptions of case retrieval, adaptation and revising algorithms. Designed and implemented CBR-based IDSS for river floodplain management uses object-oriented analysis and Java2 technology.  \keywords{intelligent decision support  system, case-based reasoning, qualitative reasoning, personalized learning,  ontology, river floodplain management } smart learning content}  \end{abstract}