Interpretable feature incorporation machine-learning framework for flood magnitude estimation.
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| Title: | Interpretable feature incorporation machine-learning framework for flood magnitude estimation. |
|---|---|
| Authors: | Ford, Emma1,2 (AUTHOR) emma.ford@hertford.ox.ac.uk, Brunner, Manuela I.3,4,5 (AUTHOR), Christensen, Hannah2 (AUTHOR), Slater, Louise1 (AUTHOR) |
| Source: | Hydrology & Earth System Sciences. 2026, Vol. 30 Issue 7, p2135-2160. 26p. |
| Subject Terms: | *Random forest algorithms, *Flood forecasting, *Climatic classification, *Hydrologic cycle, *Machine learning, *Watersheds |
| Geographic Terms: | United Kingdom |
| Abstract: | Fluvial floods pose severe socioeconomic and environmental risks and are projected to change in frequency and severity in future decades. Estimating the magnitude of extreme floods remains challenging, particularly for sparse tail events. This motivates the need to identify predictors across catchments and time. Synoptic-scale weather patterns (WPs) are often more temporally persistent and predictable than local meteorological variables, such as precipitation. However, the value of weather patterns as predictors for flood magnitude estimation is not well established. This study introduces a feature incorporation machine learning framework to quantify the relative contribution of synoptic, meteorological, and catchment controls on winter peak-over-threshold (POT) flood magnitudes (≥99 th percentile) in near-natural catchments across the United Kingdom (UK) benchmark network. We train Random Forest regression models for a pooled national sample and for multiple hydro-climatic regional samples. Model interpretability was examined using Shapley Additive Explanations (SHAP). Additionally, we analyze the conditional probabilities of the WPs co-occurring with flood magnitudes. Our results show that WPs associated with cyclonic low-pressure systems frequently coincide with flood magnitudes but add minimal value to their estimation. Model skill is dominated by static catchment attributes such as aridity and event-day precipitation in the UK model, with regional model variability in feature importance reflecting hydro-climatic contrasts. Our findings highlight the variability in model outcomes depending on the model structure and the choice of features. This study also offers methodological guidance for developing large-sample machine learning models for flood estimation that integrate atmospheric predictors with traditional hydro-meteorological and geographical variables across a feature incorporation framework. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 193224501 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Interpretable feature incorporation machine-learning framework for flood magnitude estimation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ford%2C+Emma%22">Ford, Emma</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> emma.ford@hertford.ox.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Brunner%2C+Manuela I%2E%22">Brunner, Manuela I.</searchLink><relatesTo>3,4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Christensen%2C+Hannah%22">Christensen, Hannah</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Slater%2C+Louise%22">Slater, Louise</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Hydrology+%26+Earth+System+Sciences%22">Hydrology & Earth System Sciences</searchLink>. 2026, Vol. 30 Issue 7, p2135-2160. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Flood+forecasting%22">Flood forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Climatic+classification%22">Climatic classification</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrologic+cycle%22">Hydrologic cycle</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+Kingdom%22">United Kingdom</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Fluvial floods pose severe socioeconomic and environmental risks and are projected to change in frequency and severity in future decades. Estimating the magnitude of extreme floods remains challenging, particularly for sparse tail events. This motivates the need to identify predictors across catchments and time. Synoptic-scale weather patterns (WPs) are often more temporally persistent and predictable than local meteorological variables, such as precipitation. However, the value of weather patterns as predictors for flood magnitude estimation is not well established. This study introduces a feature incorporation machine learning framework to quantify the relative contribution of synoptic, meteorological, and catchment controls on winter peak-over-threshold (POT) flood magnitudes (≥99 th percentile) in near-natural catchments across the United Kingdom (UK) benchmark network. We train Random Forest regression models for a pooled national sample and for multiple hydro-climatic regional samples. Model interpretability was examined using Shapley Additive Explanations (SHAP). Additionally, we analyze the conditional probabilities of the WPs co-occurring with flood magnitudes. Our results show that WPs associated with cyclonic low-pressure systems frequently coincide with flood magnitudes but add minimal value to their estimation. Model skill is dominated by static catchment attributes such as aridity and event-day precipitation in the UK model, with regional model variability in feature importance reflecting hydro-climatic contrasts. Our findings highlight the variability in model outcomes depending on the model structure and the choice of features. This study also offers methodological guidance for developing large-sample machine learning models for flood estimation that integrate atmospheric predictors with traditional hydro-meteorological and geographical variables across a feature incorporation framework. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193224501 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5194/hess-30-2135-2026 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 2135 Subjects: – SubjectFull: Random forest algorithms Type: general – SubjectFull: Flood forecasting Type: general – SubjectFull: Climatic classification Type: general – SubjectFull: Hydrologic cycle Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Watersheds Type: general – SubjectFull: United Kingdom Type: general Titles: – TitleFull: Interpretable feature incorporation machine-learning framework for flood magnitude estimation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ford, Emma – PersonEntity: Name: NameFull: Brunner, Manuela I. – PersonEntity: Name: NameFull: Christensen, Hannah – PersonEntity: Name: NameFull: Slater, Louise IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10275606 Numbering: – Type: volume Value: 30 – Type: issue Value: 7 Titles: – TitleFull: Hydrology & Earth System Sciences Type: main |
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