{
  "abstract": "Accurate tide forecasts are essential for coastal management, navigation, flood-risk reduction, and infrastructure protection. Observed sea level can be decomposed into astronomical and non-astronomical components, the latter mainly driven by meteorological effects. This study investigates a hybrid framework for hourly sea-level forecasting that combines harmonic analysis (HA) for the astronomical component with data-driven models for the non-astronomical contribution. The approach is evaluated at six tide-gauge stations with different tidal regimes: Venice, Trieste, Saint-Malo, Vardø, Nikiski, and Nagasaki. Four data-driven model classes are considered: (i) linear parametric models, represented by autoregressive models with exogenous variables; (ii) functional parametric models, based on functional autoregressive models with exogenous variables; (iii) semiparametric and nonlinear models, including generalized additive models and autoregressive neural networks; and (iv) a semi-functional non-standard k-nearest-neighbours approach combining similarity in recent non-astronomical trajectories and meteorological conditions. Results reveal that hybrid models reduce forecast errors by 52.9-54.9% on average relative to HA. The generalized additive model is the most competitive across locations, while k-nearest neighbours performs best at Saint-Malo and the autoregressive model with exogenous variables is favoured in Nagasaki. For Venice, an economic decision-making case study assesses the operational use of sea-level forecasts in managing the MoSE flood-barrier system.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.29827",
  "author_total": 2,
  "authors": [
    {
      "name": "Pierdomenico Duttilo"
    },
    {
      "name": "Francesco Lisi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/16904s5ps9vkh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.29827",
  "fulltext_url": null,
  "id": "16904s5ps9vkh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.29827",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2305|Hydrological Forecasting Using AI"
    ]
  ],
  "openalex": "W7214323889",
  "pdf_url": "https://arxiv.org/pdf/2609.29827",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/16904s5ps9vkh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:56Z",
      "native_id": "2609.29827",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.29827"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Hybrid Models for Short-Term Sea-Level Forecasting",
  "title_text": "Hybrid Models for Short-Term Sea-Level Forecasting",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:56Z",
  "url": "https://research.draconitic.com/paper/16904s5ps9vkh",
  "venue": "arXiv",
  "versions": []
}