Search
Application of BN in Risk Diagnostics Arising from the Degree of Urban Regeneration Area Degradation
Abstract
Urban regeneration as a complex project, generates many extremely specific threats affecting the increase of investment risk. Its unique nature causes that probability parameter, normally applied in the process of risk quantification, is extremely difficult to estimate. Due to lack of historical data urban regeneration related activities are therefore associated with uncertainty. According to the authors, a useful tool for resolving the above issues may prove to be Bayesian networks (BN). Beliefs based on expert knowledge should be considered as a subjective measure, nevertheless BN also allow to combine this information with objective results of conducted research. The authors built a model representing various urban regeneration risk areas, where the analysis covers degradation of the urban regeneration area. The article also presents selected parameters allowing for diagnostics of technical condition of buildings, road pavement and underground infrastructure in the area of urban regeneration.
Citations
-
8
CrossRef
-
0
Web of Science
-
7
Scopus
Authors (3)
Cite as
Full text
- Publication version
- Accepted or Published Version
- License
- Copyright (2017 IEEE)
Keywords
Details
- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Title of issue:
- Proceedings - 2017 Baltic Geodetic Congress (Geomatics), BGC Geomatics 2017 strony 83 - 88
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Apollo M., Grzyl B., Miszewska E..: Application of BN in Risk Diagnostics Arising from the Degree of Urban Regeneration Area Degradation, W: Proceedings - 2017 Baltic Geodetic Congress (Geomatics), BGC Geomatics 2017, 2017, ,.
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/bgc.geomatics.2017.47
- Verified by:
- Gdańsk University of Technology
seen 141 times
Recommended for you
Condition-Based Monitoring of DC Motors Performed with Autoencoders
- K. Włódarczak,
- Ł. Grzymkowski,
- T. Stefański