A Genetic Algorithm-Based Decision Support System for Transportation Infrastructure Management in Urban Areas

Author(s):  
Manoj K. Jha ◽  
Konstantinos Kepaptsoglou ◽  
Matthew Karlaftis ◽  
Jawad Abdullah
2005 ◽  
Vol 7 (1) ◽  
pp. 3-15 ◽  
Author(s):  
A. J. Abebe ◽  
R. K. Price

This paper presents the development of a decision support system (DSS) for flood warning and instantiation of restoration activities in two urban areas, the Liguria Region in Italy and the Greater Athens catchment in Greece, with the potential of extension to other locations with similar flooding problems. The tool is designed to work at the centre of a set of meteorological and hydrologic/hydraulic forecast models together with telemetric data acquisition networks. The study reveals the complexity and uncertainty involved in managing flooding in the study areas. Issues about the validity and extended benefits of the system are also discussed.


2020 ◽  
Vol 12 (2) ◽  
pp. 259 ◽  
Author(s):  
Małgorzata Sztubecka ◽  
Marta Skiba ◽  
Maria Mrówczyńska ◽  
Anna Bazan-Krzywoszańska

Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, the research problem of this article is the development of an innovative decision support system using multi-criteria analysis and Geographic Information Systems (decision support system + Geographic Information Systems = DGIS) for planning urban development. The proposed decision support system provides information to energy consumers about the location of energy efficiency improvement potential. This potential has been identified as the possibility of introducing low-energy buildings and the use of renewable energy sources. DGIS was tested in different construction areas (categories: A, B, C, D), Zielona Góra quarters. The results showed which area among the 53 quarters with a separate dominant building category was the most favorable for increasing energy efficiency, and where energy efficiency could be improved by investing in renewable energy sources, taking into account the decision-maker. The proposed DGIS system can be used by local decision-makers, allowing better action to adapt cities to climate change and to protect the environment. This approach is part of new data processing strategies to build the most favorable energy scenarios in urban areas.


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