{
  "abstract": "Sampling geometry may influence both reconstruction stability and the smoothing needed to control noise. This study distinguishes geometry-dependent failure of exact multiquadric radial basis function (RBF) interpolation from variation in validation-selected smoothing. A synthetic benchmark crossed four response families, four 16-point geometries, three slice levels, five observation regimes, and 24 repeated holdouts, yielding 17,280 method-level evaluations. Clough--Tocher cubic interpolation, exact RBF, and smoothed RBF were compared, followed by a Water Quality Portal pilot with 1,950 complete activities. On the synthetic shared-valid domain, exact RBF achieved the lowest noise-free mean RMSE (0.0206), whereas smoothed RBF performed best under noise (0.1807 versus 0.2414 for cubic and 0.6106 for exact RBF). Clustered-gap support produced the largest noisy exact-RBF RMSE (1.2706). Geometry added little explanatory power for log-transformed smoothing: the combined model had $R^2=0.4259$, compared with 0.4096 for noise alone. Reported failure-regression results suggest a stronger geometry signal, but remain unverified without their original model specification. In the environmental pilot, smoothed RBF achieved mean RMSE 0.9877 with no negative-$R^2$ failures. Conflicting responses at identical predictor coordinates made exact interpolation unsuitable. All environmental smoothed-RBF fits selected the largest candidate, preventing geometry--smoothing correlation analysis. The findings motivate geometry-aware failure screening but do not establish a transferable rule for smoothing magnitude. Independent multisite validation, appropriate coordinate scaling, joint scale--smoothing tuning, and conditioning diagnostics are needed before an adaptive policy can be claimed.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.14522",
  "author_total": 1,
  "authors": [
    {
      "name": "Mirkan Emir Sancak"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/71ca6qegx1vtz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.14522",
  "fulltext_url": null,
  "id": "71ca6qegx1vtz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.14522",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CE"
    ],
    [
      "arxiv",
      "stat.AP"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.14522",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/71ca6qegx1vtz.ris",
  "sources": [
    {
      "first_seen": "2026-11-13T06:21:06Z",
      "native_id": "2611.14522",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.14522"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Geometry-Aware Smoothing Selection for Noisy Scattered Surface Reconstruction: Evidence from Synthetic Benchmarks and Water Quality Data",
  "title_text": "Geometry-Aware Smoothing Selection for Noisy Scattered Surface Reconstruction: Evidence from Synthetic Benchmarks and Water Quality Data",
  "type": "preprint",
  "updated_at": "2026-11-13T06:21:06Z",
  "url": "https://research.draconitic.com/paper/71ca6qegx1vtz",
  "venue": "arXiv",
  "versions": []
}