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Using supervised machine learning for regional hydrological hazard estimation in metropolitan France
Study region: This study is carried out for 1929 gauged catchments in France, ranging from 1 to 10,000 km², where quality hydrometric observations are available for flood frequency analysis. Study focus: The regional estimation of hydrological hazards is studied for flood risk management and prevention in hydrology. For gauged catchments, flow quantiles can be estimated from observations using statistical approaches based on suitable probability distributions or simulation approaches based on rainfall-runoff transformation models. For ungauged catchments, the lack of hydrological observations means that we have to extrapolate our knowledge of hazards from gauged catchments to ungauged catchments, using regionalization methods. It is therefore necessary to combine regionalization methods with the implemented hazard estimation approach. In this paper, two popular machine learning methods, Random Forest and Neural Networks, are tested and compared as regionalization methods. A classical regionalization method using multiple linear regression is also applied as a benchmark to evaluate the performance of all configurations. All these regionalization methods are applied to a simulation-based approach (the SHYREG method) and to a statistical-based approach using generalized extreme value distribution (GEV). New hydrological insights: • Regionalization approaches based on multiple linear regression have limitations to explain parameters with environmental descriptors in Regional Flood Frequency Analysis (RFFA) domain. • Regionalizing RFFA parameters using Random Forest allows more explanatory variables to be considered through non-linear relationships, resulting in better parameter estimation. • Machine learning techniques can better handle environmental descriptors for regionalization, this providing a notable performance improvement, especially for the statistical approach. • The tested simulation-based approach is less sensitive to the choice of spatial interpolation method than the studied statistical approach.
Using supervised machine learning for regional hydrological hazard estimation in metropolitan France
Study region: This study is carried out for 1929 gauged catchments in France, ranging from 1 to 10,000 km², where quality hydrometric observations are available for flood frequency analysis. Study focus: The regional estimation of hydrological hazards is studied for flood risk management and prevention in hydrology. For gauged catchments, flow quantiles can be estimated from observations using statistical approaches based on suitable probability distributions or simulation approaches based on rainfall-runoff transformation models. For ungauged catchments, the lack of hydrological observations means that we have to extrapolate our knowledge of hazards from gauged catchments to ungauged catchments, using regionalization methods. It is therefore necessary to combine regionalization methods with the implemented hazard estimation approach. In this paper, two popular machine learning methods, Random Forest and Neural Networks, are tested and compared as regionalization methods. A classical regionalization method using multiple linear regression is also applied as a benchmark to evaluate the performance of all configurations. All these regionalization methods are applied to a simulation-based approach (the SHYREG method) and to a statistical-based approach using generalized extreme value distribution (GEV). New hydrological insights: • Regionalization approaches based on multiple linear regression have limitations to explain parameters with environmental descriptors in Regional Flood Frequency Analysis (RFFA) domain. • Regionalizing RFFA parameters using Random Forest allows more explanatory variables to be considered through non-linear relationships, resulting in better parameter estimation. • Machine learning techniques can better handle environmental descriptors for regionalization, this providing a notable performance improvement, especially for the statistical approach. • The tested simulation-based approach is less sensitive to the choice of spatial interpolation method than the studied statistical approach.
Using supervised machine learning for regional hydrological hazard estimation in metropolitan France
Qifan Ding (author) / Patrick Arnaud (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Using supervised machine learning for regional hydrological hazard estimation in metropolitan France
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