Machine Learning applied to groundwater‑surface water connectivity in the wet Pampa plain: groundwater level forecast and assistance for baseflow separation methods
Year: 2026
Type: article
Abstract: Setting up a physically based model to determine the connectivity between a river and its underlying aquifer can be complex, making machine learning (ML) techniques attractive for processing and exploiting time series associated with hydrometeorological variables acquired by remote sensors and in situ measurements in and around a local section of a channel. Data-driven models do not require knowledge of the basin’s characteristic physical parameters or its spatiotemporal distribution. If the inputs are of sufficient quality, the dynamics of ground water-surface water (GW-SW) exchange can be predicted at different time scales. The experience of the authors working with Ground Water Level (GWL) forecasting and baseflow filtering using the Machine Learning for Flood Forecasting (ML4FF) framework at the topographically “depressed” or wet Argentinian Pampa plain is described.
Source: Digital Water
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G8224010093
OpenAlex ID: W7204451049