{
  "abstract": "Riverine (fluvial) flooding remains a significant climate-related disaster in many countries. Extreme rainfall events caused by climate change cause significant damage and disruption to communities living near riverbanks or neighboring land areas, particularly when risk-sensitive development is ignored. This necessitates an effective mechanism to predict river flooding events and provide timely early warnings to the most vulnerable populations in high-risk flood-prone areas. The dynamics of flood risks require a localized, automated, and scalable solution that can be centrally monitored. This study presents a river flood forecasting system integrating artificial intelligence (AI) and Internet of Things (IoT). This system was deployed in one of the flood-prone river basins in Sri Lanka to assess the operational feasibility of real-time hydrological monitoring and flood-risk classification. The proposed system employed two machine learning models, Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost), for flood-risk prediction and IoT sensors (rainfall and ultrasonic sensors) for real-time hydrological data collection. The machine learning models were developed using rainfall, discharge rate, and river water level data to predict flood-risk states. A rule-based ensemble strategy was implemented by combining the outputs of LSTM and XGBoost to prioritize positive-class detection and reduce missed positive-labeled observations. Model performance was evaluated primarily using recall for the positive flood cases, while the overall accuracy was reported as the complementary metric alongside positive-class recall, providing an overall assessment of classification performance. Among the evaluated input window configurations using the same chronological holdout, the rule-based ensemble model with a 1-day input window showed the most favorable observed balance of performance metrics, achieving 100% positive-class recall, with 98.73% accuracy, 50.00% positive-class precision, and 66.67% positive-class F1-score. The 100.00% recall was based on correctly classifying all four positive-labeled observations in the held-out test set (4/4; 95% exact binomial CI: 39.8%−100.0%) and these observations occurred consecutively from November 25 to 28, 2024, and represent one continuous positive-labeled flood episode. The proposed safety-oriented AI-IoT framework demonstrates the operational feasibility of integrating real-time sensing with AI-based flood forecasting; however, longer-term field deployment, including actual flood events, is required to evaluate flood-event prediction performance under real-world flood conditions.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electrical and Telecommunication Engineering, South Eastern University of Sri Lanka , ,",
    "Business Continuity Management & Integrated Emergency Management Program, Faculty of Resilience, Rabdan Academy , ,",
    "Department of Computer and Communication Systems Engineering, University Putra Malaysia , ,",
    "Department of Civil Engineering, South Eastern University of Sri Lanka , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "T. H. S. Sankalpa"
    },
    {
      "affs": [
        0
      ],
      "name": "G. H. C. Sandaruwan"
    },
    {
      "affs": [
        1
      ],
      "name": "A. M. Aslam Saja"
    },
    {
      "affs": [
        2
      ],
      "name": "N. T. M. Sajith"
    },
    {
      "affs": [
        3
      ],
      "name": "M. F. M. Ishqy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9rczed73f10b0.bib",
  "cited_by": 0,
  "doi": "10.3389/frai.2026.1903150",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719459/?tool=EBI",
  "id": "9rczed73f10b0",
  "issns": [
    "2624-8212"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13719459",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2305|Hydrological Forecasting Using AI"
    ]
  ],
  "openalex": "W7222643542",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719459/pdf/?tool=EBI",
  "pmcid": "PMC13719459",
  "pmid": "42964103",
  "published": "2026-10-16",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/9rczed73f10b0.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:36Z",
      "native_id": "PMC:PMC13719459",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13719459"
    },
    {
      "first_seen": "2026-11-02T12:23:25Z",
      "native_id": "PMC13719459",
      "source": "pmc",
      "url": "https://doi.org/10.3389/frai.2026.1903150"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Hydrological Forecasting Using AI",
      "subfield": "environmental-science"
    }
  ],
  "title": "A recall-optimized LSTM–XGBoost ensemble model for river flood forecasting using multivariate time-series data—case study in Sri Lanka",
  "title_text": "A recall-optimized LSTM–XGBoost ensemble model for river flood forecasting using multivariate time-series data—case study in Sri Lanka",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:31Z",
  "url": "https://research.draconitic.com/paper/9rczed73f10b0",
  "venue": "Frontiers in artificial intelligence",
  "versions": []
}