{
  "abstract": "The vertical distribution of sediment concentration is a fundamental component of sediment transport hydraulics, directly impacting the estimation of sediment loads in riverine and channel systems. Therefore, accurate characterization of this distribution remains a critical challenge in hydraulic engineering. To address this, advanced intelligent modeling techniques offer a robust pathway for reliable prediction. This study evaluates the performance of six distinct modeling approaches—ORELM, ORELM-HOA, GPR, GPR-HOA, GMDH-ANFIS, and GMDH-ANFIS-HOA—to predict the relative sediment concentration distribution (C/C a ​). Results indicated that the integration of meta-heuristic optimization significantly enhances the predictive precision of the GMDH-ANFIS, ORELM, and GPR frameworks. Comparative analysis reveals that the GMDH-ANFIS-HOA model achieved the highest predictive accuracy, yielding an RMSE of 0.0434 and R ² of 0.9747 during the training phase, and an RMSE of 0.0424 and R ² of 0.9727 during the testing phase. The GPR-HOA model followed as the second-best performer. Furthermore, sensitivity analysis identified the relative depth (y/D) as the most dominant factor influencing model accuracy. These findings demonstrate that the optimized GMDH-ANFIS-HOA model provides a reliable and efficient computational tool for engineers and hydrologists, facilitating improved design and management of irrigation canals, river engineering projects, and sediment-related environmental assessments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Mohsen Nasrabadi",
      "orcid": "0000-0001-8061-8836"
    },
    {
      "name": "Yaser Mehri"
    },
    {
      "name": "Ali Abdolrazaq Sabbar"
    },
    {
      "name": "Mohammad Javad Nahvinia"
    },
    {
      "name": "Mohammad Hosein Omid"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1b3xkr2b7nrkt.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-75778-5",
  "fulltext_url": null,
  "id": "1b3xkr2b7nrkt",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-75778-5",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2303|Hydrology and Sediment Transport Processes"
    ]
  ],
  "openalex": "W7222787586",
  "pdf_url": "https://www.nature.com/articles/s41598-026-75778-5_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-17",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/1b3xkr2b7nrkt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1038/s41598-026-75778-5",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-75778-5"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Hydrology and Sediment Transport Processes",
      "subfield": "ecology"
    }
  ],
  "title": "Integrating hippopotamus optimization algorithm into machine learning frameworks for predicting suspended sediment concentrations",
  "title_text": "Integrating hippopotamus optimization algorithm into machine learning frameworks for predicting suspended sediment concentrations",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/1b3xkr2b7nrkt",
  "venue": "Scientific Reports",
  "versions": []
}