{
  "abstract": "Monthly streamflow information supports seasonal water-resource planning in tropical highland basins, where dry-season scarcity and wet-season runoff require different management responses. This study evaluated an integrated hydrological machine-learning and socio-economic water-pressure screening framework (Hydro-ML–SEWP) for the Da Tam watershed, Lam Dong Province, Vietnam. The hydrological models estimate monthly discharge conditionally on target-month rainfall and temperature together with antecedent streamflow, lagged rainfall, rolling hydroclimatic indicators, and seasonal cyclic terms; they are therefore within-month estimators rather than strict lead-time forecasts. A seasonal-naive benchmark, Ridge regression, support vector regression with a radial basis function kernel (SVR-RBF), Random Forest, and Gradient Boosting were assessed using a chronological 2019–2024 hold-out period and rolling-origin evaluation for 2011–2024. Ridge regression gave the highest R 2 and lowest RMSE in both evaluations (hold-out R 2 = 0.863, RMSE = 0.904 m3 s−1; rolling-origin R 2 = 0.831, RMSE = 1.047 m3 s−1), whereas Random Forest gave the lowest MAE and the highest normalized composite score. Target-month rainfall, seasonal cyclicity, 1-month lagged discharge, and 1-month lagged rainfall represented 77.1% of the Random Forest impurity-based importance, although correlated predictors and the use of concurrent rainfall limit causal interpretation. Screening of the 1995–2024 drainage-area-transferred streamflow series identified February–March and September–October as the principal low-flow and high-flow periods. Because the source dataset does not identify the reference gauge and the available socio-economic records are not temporally aligned across all four proposed SEWP components, absolute discharge magnitudes and annual SEWP values are not treated as fully verified basin-specific quantities. The framework is therefore presented as a transparent conditional-estimation and scenario-screening approach whose operational use requires documented reference-basin metadata, target-month meteorological forecasts or lag-only predictors, and watershed-scale socio-economic data.",
  "abstract_shown": true,
  "affiliations": [
    "University of Science – Viet Nam National University",
    "Ho Chi Minh City University of Technology and Engineering",
    "Ho Chi Minh City University of Natural Resources and Environment"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Anh The Pham"
    },
    {
      "affs": [
        0
      ],
      "name": "Canh Thanh Truong"
    },
    {
      "affs": [
        1
      ],
      "name": "An Xuan Bui"
    },
    {
      "affs": [
        2
      ],
      "name": "Bao Quoc Tran"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0s0req81tgk58.bib",
  "cited_by": 0,
  "doi": "10.1007/s44533-026-00087-9",
  "fulltext_url": "https://doi.org/10.1007/s44533-026-00087-9",
  "id": "0s0req81tgk58",
  "issns": [
    "2357-0008",
    "1110-4929"
  ],
  "landing_url": "https://doi.org/10.1007/s44533-026-00087-9",
  "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",
      "TC1-978"
    ],
    [
      "lcc",
      "TD1-1066"
    ],
    [
      "openalex",
      "2305|Hydrological Forecasting Using AI"
    ]
  ],
  "openalex": "W7224434928",
  "pdf_url": "https://link.springer.com/content/pdf/10.1007/s44533-026-00087-9.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Springer Nature",
  "ris": "https://research.draconitic.com/paper/0s0req81tgk58.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T14:21:23Z",
      "native_id": "0d81da79d208458faa9b8db087c1eabb",
      "source": "doaj",
      "url": "https://doi.org/10.1007/s44533-026-00087-9"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Hydrological Forecasting Using AI",
      "subfield": "environmental-science"
    }
  ],
  "title": "Interpretable monthly streamflow estimation for seasonal water-resource management in highland watershed",
  "title_text": "Interpretable monthly streamflow estimation for seasonal water-resource management in highland watershed",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T14:20:50Z",
  "url": "https://research.draconitic.com/paper/0s0req81tgk58",
  "venue": "Water Science",
  "versions": []
}