Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Preprints |
| DOI | 10.2139/ssrn.5135378 |
| Titel (primär) | Distilling the Pareto optimal front into actionable insights |
| Autor | White, S.E.; Witing, F.
; Wittekind, C.I.H.; Volk, M.; Strauch, M.
|
| Quelle | SSRN |
| Erscheinungsjahr | 2025 |
| Department | CLE |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Abstract | The growing importance of multi-objective optimization in environmental decision making highlights the need for simplifying and interpreting highly dimensional Pareto optimal data, which often constitutes a cognitive overload for both scientists and stakeholders. This paper presents PyretoClustR, a modular framework developed using Python and R for post-processing Pareto optimal solutions in multi-objective environmental optimization. The framework involves steps such as variable reduction, principal component analysis, clustering with k-means and k-medoids, outlier handling, and visualization. A case study from the BiodivERsA project "TALE - Towards multifunctional Agricultural Landscapes in Europe" illustrates the effectiveness of PyretoClustR, highlighting trade-offs between agricultural productivity, biodiversity, water quality, and ecological flow. The framework’s graphical clustering simplifies complex solution sets, improves stakeholder understanding of trade-offs and synergies, and is adaptable to various environmental datasets and decision-making scenarios. The ultimate goal is to foster a deeper practical understanding of multi-objective optimization results for informed decision making. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=30516 |
| White, S.E., Witing, F., Wittekind, C.I.H., Volk, M., Strauch, M. (2025): Distilling the Pareto optimal front into actionable insights SSRN 10.2139/ssrn.5135378 |
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