Ontology For Flood Management: A Proposal

Floods are very complex phenomena involving a large number of players. In this context not only the hydrologic and hydraulic studies are required, but also those related to vulnerability assessment, town and emergency planning as well as all the matters related to the economic issues: damage, insurance and the psychological diseases which can arise after flood events. Thus, people with widely different roles, backgrounds, and profiles are in charge of each phase of a large number of complex procedures. Not surprisingly, communication problems often arise because different names may be used to refer to the same concepts. Moreover, relevant information can vary, depending on the user. Effective communication is essential since the results produced by a person in a certain phase of the process can be the input that another person needs in the next phase, i.e. raw data versus elaborated data, forecasting versus impact evaluation, level of detail, hazard assessment, vulnerability assessment, economic evaluation, environmental assessment, etc. Ontologies, which can be defined as an explicit representation of a conceptualization, are tools that can be used to solve such very complex and interdisciplinary problems because they specify a conceptual framework or terminology. The paper introduces an ontology for floods. This ontology is developed using the existing Environmental Impact Assessment (EIA) ontology and it is represented with OWL-DL (Web Ontology Language – Description Logics). Among the different causes for flood, only those related to river overflows are taken into account in the ontology so far. It is shown as ontologies are useful Flood Recovery Innovation and Response III 3 www.witpress.com, ISSN 1743-3541 (on-line) WIT Transactions on Ecology and The Environment, Vol 159, © 201 WIT Press 2 doi:10.2495/FRIAR120011 because they represent the most meaningful knowledge associated with a problem. Moreover, they are also useful because they permit reasoning and inference processes. Inference mechanisms make recommendation procedures possible.