A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Hybrid modeling of karstic springs: Error correction of conceptual reservoir models with machine learning
Accurate spring discharge modeling and prediction is crucial for water management, helping authorities optimize use, manage variability, and prepare for droughts. Developing reliable simulation and forecasting tools is essential for effective management of groundwater resources from karstic springs. Although hybrid modeling approaches have been explored in hydrology, their application to spring discharge modeling is underexplored. Previous studies have focused on conceptual/distributed or data-driven models separately, missing the potential advantages of combining them. This creates a research gap in exploring the benefits of hybrid models for spring discharge. This study developed a hybrid model combining a conceptual GR5J model with Random Forests to simulate spring discharge from Bordeaux's largest karst aquifer. Model performance was assessed through comparison with the individual GR5J, RF, and benchmark models (weekly average of observed values). The hybrid model outperformed all models. Evaluation using actual meteorological data found the hybrid model achieved the highest accuracy by reducing GR5J simulation errors by 22%. When considering meteorological uncertainty, the hybrid model outperformed the individual GR5J, RF and benchmark models by 11, 30 and 47% respectively. The study findings suggest combining conceptual and machine learning approaches can improve spring discharge simulations, opening promising opportunities for enhanced forecasting in karst aquifers. HIGHLIGHTS Proposing a novel hybrid model that integrates conceptual hydrological model (GR5J) with a machine learning technique (random forests) to simulate spring discharge from a karst aquifer.; The hybrid model outperforms the conceptual and machine learning models, as well as a naïve benchmark model (average values).; Meteorological uncertainty remains the major component of model error when considering future simulation periods.;
Hybrid modeling of karstic springs: Error correction of conceptual reservoir models with machine learning
Accurate spring discharge modeling and prediction is crucial for water management, helping authorities optimize use, manage variability, and prepare for droughts. Developing reliable simulation and forecasting tools is essential for effective management of groundwater resources from karstic springs. Although hybrid modeling approaches have been explored in hydrology, their application to spring discharge modeling is underexplored. Previous studies have focused on conceptual/distributed or data-driven models separately, missing the potential advantages of combining them. This creates a research gap in exploring the benefits of hybrid models for spring discharge. This study developed a hybrid model combining a conceptual GR5J model with Random Forests to simulate spring discharge from Bordeaux's largest karst aquifer. Model performance was assessed through comparison with the individual GR5J, RF, and benchmark models (weekly average of observed values). The hybrid model outperformed all models. Evaluation using actual meteorological data found the hybrid model achieved the highest accuracy by reducing GR5J simulation errors by 22%. When considering meteorological uncertainty, the hybrid model outperformed the individual GR5J, RF and benchmark models by 11, 30 and 47% respectively. The study findings suggest combining conceptual and machine learning approaches can improve spring discharge simulations, opening promising opportunities for enhanced forecasting in karst aquifers. HIGHLIGHTS Proposing a novel hybrid model that integrates conceptual hydrological model (GR5J) with a machine learning technique (random forests) to simulate spring discharge from a karst aquifer.; The hybrid model outperforms the conceptual and machine learning models, as well as a naïve benchmark model (average values).; Meteorological uncertainty remains the major component of model error when considering future simulation periods.;
Hybrid modeling of karstic springs: Error correction of conceptual reservoir models with machine learning
N. Bouhafa (author) / C. Sakarovitch (author) / Laura Lalague (author) / F. Goulard (author) / Alexandre Pryet (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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