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Development of categorical boosting regression on peak discharge from earth-rock dam failures assessment.

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Τίτλος: Development of categorical boosting regression on peak discharge from earth-rock dam failures assessment.
Συγγραφείς: Hai, Qing, Yu, Liangliang, Li, Na
Πηγή: Canadian Journal of Civil Engineering; 10/2/2026, Vol. 53, p1-13, 13p
Θεματικοί όροι: Machine learning, Boosting algorithms, Metaheuristic algorithms, Dam failures, Flood control
Περίληψη: Accurate peak flow (Qp) estimation at earth-rock dam breaches is vital for flood management. The study collected 162 cases, removed three outliers using IQR, leaving 159 records for analysis. The model received 80% of the dataset for training and the remaining 20% as test material. The essential input parameters included the dam type, together with failure mode, dam height, and water height above the breach invert and stored water volume above the breach at failure. The research evaluates categorical boosting regression (CBR) as an ML technique that predicts peak discharge values. The research team applied Starfish Optimization (StFO) and Tyrannosaurus Rex Optimization (TyRO) metaheuristic optimizers to select hyperparameters because these choices determine how well the model will perform. Results show that CBRStFO outperforms CBRTyRO, achieving higher R2 values in both training (0.979) and testing (0.967), with prediction improvements of about 4%, demonstrating superior accuracy and reliability. [ABSTRACT FROM AUTHOR]
Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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DbLabel: Complementary Index
An: 197548610
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1162.43029785156
IllustrationInfo
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  Label: Title
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  Data: Development of categorical boosting regression on peak discharge from earth-rock dam failures assessment.
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  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hai%2C+Qing%22">Hai, Qing</searchLink><br /><searchLink fieldCode="AR" term="%22Yu%2C+Liangliang%22">Yu, Liangliang</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Na%22">Li, Na</searchLink>
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  Data: Canadian Journal of Civil Engineering; 10/2/2026, Vol. 53, p1-13, 13p
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Dam+failures%22">Dam failures</searchLink><br /><searchLink fieldCode="DE" term="%22Flood+control%22">Flood control</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate peak flow (Qp) estimation at earth-rock dam breaches is vital for flood management. The study collected 162 cases, removed three outliers using IQR, leaving 159 records for analysis. The model received 80% of the dataset for training and the remaining 20% as test material. The essential input parameters included the dam type, together with failure mode, dam height, and water height above the breach invert and stored water volume above the breach at failure. The research evaluates categorical boosting regression (CBR) as an ML technique that predicts peak discharge values. The research team applied Starfish Optimization (StFO) and Tyrannosaurus Rex Optimization (TyRO) metaheuristic optimizers to select hyperparameters because these choices determine how well the model will perform. Results show that CBR<superscript>StFO</superscript> outperforms CBR<superscript>TyRO</superscript>, achieving higher R<superscript>2</superscript> values in both training (0.979) and testing (0.967), with prediction improvements of about 4%, demonstrating superior accuracy and reliability. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1139/cjce-2025-0594
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 13
        StartPage: 1
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Boosting algorithms
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Dam failures
        Type: general
      – SubjectFull: Flood control
        Type: general
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      – TitleFull: Development of categorical boosting regression on peak discharge from earth-rock dam failures assessment.
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            NameFull: Yu, Liangliang
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              M: 10
              Text: 10/2/2026
              Type: published
              Y: 2026
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              Value: 53
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