| Title | An ontology framework for flooding forecasting |
|---|---|
| Publication Type | Articolo su Rivista peer-reviewed |
| Year of Publication | 2014 |
| Authors | Agresta, A., Fattoruso G., Pollino Maurizio, Pasanisi F., Tebano C., De Vito S., and Di Francia G. |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 8582 LNCS |
| Pagination | 417-428 |
| ISBN Number | 9783319091464 |
| ISSN | 03029743 |
| Keywords | Computer simulation, Continuous measurements, Expert systems, Flooding risks, Floods, Highly urbanized areas, Knowledge base, Models, Ontology, real time, Semantic constraints, Semantic Web technology, Sensor networks, Water parameters, Weather forecasting |
| Abstract | Floods can cause significant damage and disruption as they often affect highly urbanized areas. The capability of knowledge using and sharing is the main reason why the ontologies are suited for supporting the phases of forecasting in (near-) real time disastrous flooding events and managing the flooding alert and emergency. This research work develops an ontology, FloodOntology for floods forecasting based on continuous measurements of water parameters gathered in the watersheds and in the sewers and simulation models. Concepts are captured across the main involved domains i.e. hydrological/ hydraulic domains and SN-based monitoring domain. Classes hierarchies, properties and semantic constraints are defined related to all involved entities, obtaining a structured and unified knowledge-base on the flooding risk forecasting, to be integrated in expert systems. © 2014 Springer International Publishing. |
| Notes | cited By 0; Conference of 14th International Conference on Computational Science and Its Applications, ICCSA 2014 ; Conference Date: 30 June 2014 Through 3 July 2014; Conference Code:106576 |
| URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904859470&doi=10.1007%2f978-3-319-09147-1_30&partnerID=40&md5=8fd9d5550f81ff9c22085643b54e127f |
| DOI | 10.1007/978-3-319-09147-1_30 |
| Citation Key | Agresta2014417 |
