Modeling and optimizing transmission lines with GIS and Multi-Criteria Decision Analysis
Autor(en):
Zusammenfassung
In planning transmission lines with the use of Geographic Information Systems, the use of the Least Cost Path (LCP) algorithm has been established while relevant criteria are modeled using Multi-Criteria Decision Analysis (MCDA). Despite their established use, this combination (MCDA/LCP) often leads to results that do not correspond to realistic conditions. Therefore, the MCDA/LCP computation must usually be optimized on an algorithmic level as well as on the decision model and the underlying data relevant for the MCDA. The current paper presents the state-of-the-art of an ongoing research project that aims to solve these issues. First results are promising since a stable algorithm has been developed that computes a cost surface, a Least Cost Corridor (LCC), a LCP, and the transmission towers' positions by simple additive weighting based on user's weights. Optimizations on the MCDA models have already been implemented and tested. The findings are integrated into a 3D Decision Support System which aims at facilitating the work of TL planners by realistic modeling and by reducing the approval process for new TL.
- Vollständige Referenz
- BibTeX
Schito, J.,
(2017).
Modeling and optimizing transmission lines with GIS and Multi-Criteria Decision Analysis.
it - Information Technology: Vol. 59, No. 5.
Berlin:
De Gruyter.
(S. 1).
DOI: 10.1515/itit-2016-0057
@article{mci/Schito2017,
author = {Schito, Joram},
title = {Modeling and optimizing transmission lines with GIS and Multi-Criteria Decision Analysis},
journal = {it - Information Technology},
volume = {59},
number = {5},
year = {2017},
,
pages = { 1 } ,
doi = { 10.1515/itit-2016-0057 }
}
author = {Schito, Joram},
title = {Modeling and optimizing transmission lines with GIS and Multi-Criteria Decision Analysis},
journal = {it - Information Technology},
volume = {59},
number = {5},
year = {2017},
,
pages = { 1 } ,
doi = { 10.1515/itit-2016-0057 }
}
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Mehr Information
ISSN: 1611-2776
Datum: 2017
Sprache:
(en)
(en)
Typ: Text/Journal Article

