Details
| Original language | English |
|---|---|
| Title of host publication | Knowledge-Based Software Engineering - 11th Joint Conference, JCKBSE 2014, Proceedings |
| Publisher | Springer Verlag |
| Pages | 195-213 |
| Number of pages | 19 |
| ISBN (print) | 9783319118536 |
| Publication status | Published - 2014 |
| Event | 11th Joint Conference on Knowledge-Based Software Engineering, JCKBSE 2014 - Volgograd, Russian Federation Duration: 17 Sept 2014 → 20 Sept 2014 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Volume | 466 CCIS |
| ISSN (Print) | 1865-0929 |
Abstract
Decision making in river floodplain management is 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
- case-based reasoning, intelligent decision support system, ontology, qualitative reasoning, river floodplain management
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Mathematics(all)
- General Mathematics
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Knowledge-Based Software Engineering - 11th Joint Conference, JCKBSE 2014, Proceedings. Springer Verlag, 2014. p. 195-213 (Communications in Computer and Information Science; Vol. 466 CCIS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Intelligent Decision Support System for River Floodplain Management
AU - Wriggers, Peter
AU - Kultsova, Marina
AU - Kapysh, Alexander
AU - Kultsov, Anton
AU - Zhukova, Irina
PY - 2014
Y1 - 2014
N2 - Decision making in river floodplain management is 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.
AB - Decision making in river floodplain management is 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.
KW - case-based reasoning
KW - intelligent decision support system
KW - ontology
KW - qualitative reasoning
KW - river floodplain management
UR - http://www.scopus.com/inward/record.url?scp=84907354765&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-11854-3_18
DO - 10.1007/978-3-319-11854-3_18
M3 - Conference contribution
AN - SCOPUS:84907354765
SN - 9783319118536
T3 - Communications in Computer and Information Science
SP - 195
EP - 213
BT - Knowledge-Based Software Engineering - 11th Joint Conference, JCKBSE 2014, Proceedings
PB - Springer Verlag
T2 - 11th Joint Conference on Knowledge-Based Software Engineering, JCKBSE 2014
Y2 - 17 September 2014 through 20 September 2014
ER -