{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.16565",
  "author_total": 4,
  "authors": [
    {
      "name": "Hidekazu Yoshioka"
    },
    {
      "name": "Motoh Tsujimura"
    },
    {
      "name": "Yumi Yoshioka"
    },
    {
      "name": "Ayumi Hashiguchi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/61tm3yan3td3f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.16565",
  "fulltext_url": null,
  "id": "61tm3yan3td3f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.16565",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2606.16565",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/61tm3yan3td3f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:00Z",
      "native_id": "2606.16565",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.16565"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Distributionally robust decision-making under ambiguity: case study of water environmental management",
  "title_text": "Distributionally robust decision-making under ambiguity: case study of water environmental management",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:00Z",
  "url": "https://research.draconitic.com/paper/61tm3yan3td3f",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1007/s43069-026-00683-6",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}