How do machine learning models deal with inter-catchment groundwater flows? - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Conference Papers Year : 2023

How do machine learning models deal with inter-catchment groundwater flows?

Abstract

Machine learning models have recently gained popularity in hydrological modelling at the catchment scale, fuelled by the increasing availability of large-sample data sets and the increasing accessibility of deep learning frameworks, computing environments, and open-source tools. In particular, several large-sample studies at daily and monthly time scales across the globe showed successful applications of the LSTM architecture as a regional model learning of the hydrological behaviour at the catchment scale. Yet, a deeper understanding of how machine learning models close the water balance and how they deal with inter-catchment groundwater flows is needed to move towards better process understanding. We investigate the performance and behaviour of the LSTM architecture at a monthly time step on a large sample French data set coined CHAMEAU – following the CAMELS initiative. To provide additional information to the learning step of the LSTM, we use the parameter sets and fluxes from the conceptual GR2M model that has a dedicated formulation to deal with inter-catchment groundwater flows. We see this study as a contribution towards the development of hybrid hydrological models.

Domains

Hydrology
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Dates and versions

hal-04088274 , version 1 (04-05-2023)

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Nicolas Weaver, Taha-Abderrahman El Ouahabi, Thibault Hallouin, François Bourgin, Charles Perrin, et al.. How do machine learning models deal with inter-catchment groundwater flows?. EGU General Assembly 2023, Apr 2023, Vienna, Austria. ⟨10.5194/egusphere-egu23-5199⟩. ⟨hal-04088274⟩
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