Distributionally robust decision-making under ambiguity: case study of water environmental management

@misc{yoshioka2026distributionally,
  title = {{Distributionally robust decision-making under ambiguity: case study of water environmental management}},
  author = {Hidekazu Yoshioka and Motoh Tsujimura and Yumi Yoshioka and Ayumi Hashiguchi},
  howpublished = {arXiv},
  year = {2026},
  month = jun,
  doi = {10.48550/arxiv.2606.16565},
  eprint = {2606.16565},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.16565},
}

TY  - UNPB
TI  - Distributionally robust decision-making under ambiguity: case study of water environmental management
AU  - Hidekazu Yoshioka
AU  - Motoh Tsujimura
AU  - Yumi Yoshioka
AU  - Ayumi Hashiguchi
PY  - 2026
DA  - 2026/06/15
PB  - arXiv
DO  - 10.48550/arxiv.2606.16565
UR  - https://arxiv.org/abs/2606.16565
AB  - Decision-making under uncertainty is ubiquitous in environmental project planning. Environmental processes such as a streamflow discharge often present a subexponential memory, where the autocorrelation persists for a long time. In addition, optimization problems driven by environmental processes encounter the issue of model ambiguity because of a lack of sufficient data for model identification. To facilitate decision-making for the management of aquatic environments (e.g., flood mitigation, water abstraction for hydropower generation), we formulate a unified distributionally robust stochastic optimization problem based on a mixed moving average (MMA) process. The MMA process is a superposition of infinite-dimensional affine stochastic processes that is seemingly complex, but the affine property helps with the formulation and computation of the optimization. Our problem is based on a convex objective with a nonsmooth conditional value-at-risk measure. We present a convergent regularization to obtain its smooth and strictly convex counterpart. The model ambiguity is represented as a distortion of the probability density of the target dynamics, and it is penalized by a divergence with which the optimization problem remains convex and becomes computable. As a case study, we apply the optimization problem to two cases with identified parameter values. The performance of the optimized dynamics is evaluated through a statistical simulation. This paper serves as a multidisciplinary work covering both the theory and application of distributionally robust optimization.
ER  -