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Peer-reviewedCC BY 4.0Scientific Reports

Integrating hippopotamus optimization algorithm into machine learning frameworks for predicting suspended sediment concentrations

  • Mohsen Nasrabadi
  • Yaser Mehri
  • Ali Abdolrazaq Sabbar
  • Mohammad Javad Nahvinia
  • Mohammad Hosein Omid
Open at sourcePDFDOICite

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.

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Cite

BibTeXRIS
BibTeX
@article{nasrabadi2026integrating,
  title = {{Integrating hippopotamus optimization algorithm into machine learning frameworks for predicting suspended sediment concentrations}},
  author = {Mohsen Nasrabadi and Yaser Mehri and Ali Abdolrazaq Sabbar and Mohammad Javad Nahvinia and Mohammad Hosein Omid},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-75778-5},
  url = {https://www.nature.com/articles/s41598-026-75778-5},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Integrating hippopotamus optimization algorithm into machine learning frameworks for predicting suspended sediment concentrations
AU  - Mohsen Nasrabadi
AU  - Yaser Mehri
AU  - Ali Abdolrazaq Sabbar
AU  - Mohammad Javad Nahvinia
AU  - Mohammad Hosein Omid
PY  - 2026
DA  - 2026/10/17
JO  - Scientific Reports
DO  - 10.1038/s41598-026-75778-5
UR  - https://www.nature.com/articles/s41598-026-75778-5
AB  - 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.
SN  - 2045-2322
ER  -