Optimization of black-box models with uncertain climatic inputs-Application to sunflower ideotype design

Accounting for the interannual climatic variations is a well-known issue for simulation-based studies of environmental systems. It often requires intensive sampling (e.g., averaging the simulation outputs over many climatic series), which hinders many sequential processes, in particular optimization...

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Bibliographic Details
Published in:PloS one 2017-05, Vol.12 (5), p.e0176815-e0176815
Main Authors: Picheny, Victor, Trépos, Ronan, Casadebaig, Pierre
Format: Article
Language:English
Subjects:
Agronomy
Algorithms
Aversion
Biology and Life Sciences
Climate
Climate change
Climate cycles
Climate models
Climate variations
Climatic data
Climatic variations
Clustering
Computer applications
Computer Simulation
Crops, Agricultural - growth & development
Data processing
Design optimization
Earth Sciences
Environment
Helianthus - growth & development
Life Sciences
Models, Theoretical
Multiple objective analysis
Optimization theory
Physical Sciences
Physiology
Random variables
Research and Analysis Methods
Sampling
Solvers
Uncertainty
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Summary:Accounting for the interannual climatic variations is a well-known issue for simulation-based studies of environmental systems. It often requires intensive sampling (e.g., averaging the simulation outputs over many climatic series), which hinders many sequential processes, in particular optimization algorithms. We propose here an approach based on a subset selection in a large basis of climatic series, using an ad-hoc similarity function and clustering. A non-parametric reconstruction technique is introduced to estimate accurately the distribution of the output of interest using only the subset sampling. The proposed strategy is non-intrusive and generic (i.e. transposable to most models with climatic data inputs), and can be combined to most "off-the-shelf" optimization solvers. We apply our approach to sunflower ideotype design using the crop model SUNFLO. The underlying optimization problem is formulated as a multi-objective one to account for risk-aversion. Our approach achieves good performances even for limited computational budgets, outperforming significantly standard strategies.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0176815