{
  "abstract": "Accurate water quality forecasting—specifically tracking phytoplankton dynamics like chlorophyll a —is crucial for safeguarding aquatic ecosystems and global water security. Deep temporal architectures offer powerful non-linear modeling capabilities, promising to overcome the parameterization bottlenecks of traditional process-based models. However, current applications treat these models as black boxes, leaving it obscure how specific architectural modules interact with the intrinsic ecological time scales governing aquatic environments. Here, we reveal a fundamental architectural–ecological duality in water quality forecasting by systematically dissecting nine prediction architectures—spanning classic baselines to modern deep models—across two ecologically contrasting reservoirs. Using chlorophyll a dynamics to represent integrated system responses, we show that patch embedding serves as a universal temporal operator, mitigating noise and enhancing forecasting accuracy by up to 10.1% when integrated into standard baselines. Crucially, inter-variable modeling strategies dictate the effective forecast horizon: channel-independent architectures dominate short-term (1–7 days) chlorophyll a forecasting (Nash–Sutcliffe efficiency up to 0.88) by capturing biomass self-persistence, whereas cross-variable attention architectures excel in medium-term (8–15 days) prediction (efficiency up to 0.83) by uncoupling delayed nutrient–temperature interactions. Explainable AI further confirms a temporal shift in feature reliance from current biomass to lagged drivers over extended horizons. Beyond water quality, this mechanistic alignment between deep learning modules and ecological time scales establishes a scalable blueprint for building interpretable, horizon-adaptive AI frameworks across complex climate and environmental systems.",
  "abstract_shown": true,
  "affiliations": [
    "Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen, 361102, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, 361102, China",
    "Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen, 361102, China; Corresponding author.",
    "College of Life and Environmental Science, Wenzhou University, Wenzhou, 325035, China; National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution, Wenzhou University, Wenzhou, 325035, China",
    "Department of Earth and Planetary Sciences, The University of Hong Kong, 999077, Hong Kong, China",
    "State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 211135, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Helmholtz-Centre for Environmental Research-UFZ, Department Lake Research, Magdeburg, 39114, Germany",
    "Helmholtz-Centre for Environmental Research-UFZ, Department Lake Research, Magdeburg, 39114, Germany; Brandenburg University of Technology, Faculty Environment and Natural Sciences, Cottbus, 03046, Germany"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yiqi Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "Mingzhen Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhongyao Liang"
    },
    {
      "affs": [
        2
      ],
      "name": "Fan Qu"
    },
    {
      "affs": [
        3
      ],
      "name": "Yao Wang"
    },
    {
      "affs": [
        4
      ],
      "name": "Xiangzhen Kong"
    },
    {
      "affs": [
        5
      ],
      "name": "Karsten Rinke"
    },
    {
      "affs": [
        0
      ],
      "name": "Nengwang Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cd2etxhwrgxp6.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ese.2026.100755",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S2666498426001006",
  "id": "cd2etxhwrgxp6",
  "issns": [
    "2666-4984"
  ],
  "landing_url": "https://doi.org/10.1016/j.ese.2026.100755",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "GE1-350"
    ],
    [
      "lcc",
      "TD1-1066"
    ],
    [
      "openalex",
      "2305|Hydrological Forecasting Using AI"
    ]
  ],
  "openalex": "W7204089617",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S2666498426001006/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/cd2etxhwrgxp6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:33Z",
      "native_id": "8f82096652764b7cac2b5db870606541",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.ese.2026.100755"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Hydrological Forecasting Using AI",
      "subfield": "environmental-science"
    }
  ],
  "title": "Deep forecasting architectures mirror ecological time scales in water quality prediction",
  "title_text": "Deep forecasting architectures mirror ecological time scales in water quality prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:33Z",
  "url": "https://research.draconitic.com/paper/cd2etxhwrgxp6",
  "venue": "Environmental Science and Ecotechnology",
  "versions": []
}