Mining Linguistic Associations for Emergent Flood Prediction Adjustment

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F13%3AA14018JV" target="_blank" >RIV/61988987:17610/13:A14018JV - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Mining Linguistic Associations for Emergent Flood Prediction Adjustment
Original language description
Floods belong to the most hazardous natural disasters and their disaster management heavily relies on precise forecasts of water flow rates. These forecasts are provided by sophisticated physical models based on differential equations. However, these models do depend on unreliable inputs. Thus, an appropriate data-mining analysis of the physical model and its precision based on features that determine distinct situations seems to be helpful in adjusting the physical model. An of application of fuzzy GUHA method in flood peak prediction is presented in this paper. Measured water flow rate data from a system for emergent flood predictions were used in order to mine fuzzy association rules expressed in natural language. The found associations were interpreted as fuzzy IF-THEN rules and used to predict expected time shift of flow rate peaks forecasted by the given physical model.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BA - General mathematics
OECD FORD branch
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Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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