{
  "abstract": "Groundwater-level prediction supports water-resource management in overexploited aquifers, yet conventional data-driven models often struggle to represent pumping disturbances and contrasting shallow- and deep-aquifer responses. Using groundwater levels, meteorological variables, and agricultural pumping data from four shallow and four deep wells in Guantao County, North China Plain, we developed an aquifer-differentiated process-guided gated recurrent unit model for daily and dekadal (10-day) prediction. Compared with a conventional gated recurrent unit model, the process-guided model reduced group-mean root-mean-square error by 7.2% and 6.1% for shallow and deep wells at the daily scale and by 4.0% and 2.1% at the dekadal scale, respectively. Deep-well performance depended on the pumping-response window, with 60-day averaged pumping providing a stable configuration. These incremental gains show that incorporating pumping disturbances and aquifer-specific response differences as process guidance can improve groundwater-level representation and provide a modeling approach for groundwater management in agricultural abstraction areas.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Xiandong Xie"
    },
    {
      "name": "Baizhong Yan"
    },
    {
      "name": "Tian Wang"
    },
    {
      "name": "Chuanlong Han"
    },
    {
      "name": "Cong Wei"
    },
    {
      "name": "Yanliang Zhai"
    },
    {
      "name": "Jialong Lv"
    },
    {
      "name": "Mingrong Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5e4bz2q8xhkt6.bib",
  "cited_by": 0,
  "doi": "10.1016/j.isci.2026.117832",
  "fulltext_url": "https://europepmc.org/articles/PMC13677239",
  "id": "5e4bz2q8xhkt6",
  "issns": [
    "2589-0042"
  ],
  "landing_url": "https://www.cell.com/iscience/fulltext/S2589-0042(26)03213-X",
  "language": "eng",
  "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": null,
  "native_subjects": [
    [
      "heading",
      "Aquatic science"
    ],
    [
      "heading",
      "Environmental management"
    ],
    [
      "heading",
      "Environmental resource"
    ],
    [
      "openalex",
      "2305|Hydrological Forecasting Using AI"
    ]
  ],
  "openalex": "W7221123846",
  "pdf_url": "https://europepmc.org/articles/PMC13677239?pdf=render",
  "pmcid": "PMC13677239",
  "pmid": "42894881",
  "published": "2026-10-09",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/5e4bz2q8xhkt6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:16Z",
      "native_id": "10.1016/j.isci.2026.117832",
      "source": "feed-cell",
      "url": "https://www.cell.com/iscience/fulltext/S2589-0042(26)03213-X"
    },
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1016/j.isci.2026.117832",
      "source": "crossref",
      "url": "https://linkinghub.elsevier.com/retrieve/pii/S258900422603213X"
    },
    {
      "first_seen": "2026-10-30T12:19:33Z",
      "native_id": "MED:42894881",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42894881"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Hydrological Forecasting Using AI",
      "subfield": "environmental-science"
    }
  ],
  "title": "Aquifer-differentiated process-guided GRU for multi-timescale groundwater-level prediction",
  "title_text": "Aquifer-differentiated process-guided GRU for multi-timescale groundwater-level prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:33Z",
  "url": "https://research.draconitic.com/paper/5e4bz2q8xhkt6",
  "venue": "iScience",
  "versions": []
}