Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.envsoft.2025.106508 |
Licence  |
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| Title (Primary) | Distilling the Pareto optimal front into actionable insights |
| Author | White, S.E.; Witing, F.
; Wittekind, C.I.H.; Volk, M.; Strauch, M.
|
| Source Titel | Environmental Modelling & Software |
| Year | 2025 |
| Department | CLE |
| Volume | 191 |
| Page From | art. 106508 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.5281/zenodo.14761001 |
| Supplements | https://ars.els-cdn.com/content/image/1-s2.0-S1364815225001926-mmc1.docx |
| Keywords | Multi-objective optimization; land management; clustering; visualization; Pareto pruning; PyretoClustR |
| 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 frameworkâs 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. |
| Persistent UFZ Identifier | 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 Environ. Modell. Softw. 191 , art. 106508 10.1016/j.envsoft.2025.106508 |
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