RT Journal Article
T1 A surrogate approach to model groundwater level in time and space based on tree regressors
A1 Martínez Santos, Pedro
A1 Gómez-Escalonilla Canales, Víctor
A1 Díaz Alcaide, Silvia
A1 Rodríguez del Rosario, Manuel
A1 Aguilera Alonso, Héctor
AB Groundwater is a crucial resource for humans and the environment. Protection of groundwater supplies requires tools to explore and understand the behavior of aquifers. This research presents a machine learning approach to predict groundwater levels in time and space based on tree regressors. Covariates comprise dynamic and static items, including spatial coordinates, aquifer properties, timestamps, recharge and pumping data. Certain dynamic variables also include a subset of lag periods to depict seasonality. Algorithms are tested on a set of climatic scenarios in order to observe their ability to predict stable, declining and recovering groundwater trends. Random forest, ExtraTrees and gradient boosting regression behave rather similarly, with generalization scores in excess of 0.95 for wet, dry and average climatic conditions. Predictive accuracy exceeds 0.85 when comparing their long-term forecasts with unseen predictions computed by means of a calibrated numerical model. Feature importance analysis, coupled with the outcomes of partial dependence plots, suggests that tree regressors are able to capture the relevance of dynamic and static variables, thus making the results extrapolable not only in time, but also in space. Outcomes open up an alternative to model groundwater-related variables without necessarily relying on flow and transport equations. This approach can be readily extrapolated to other settings and might offer a rapid means to obtain useful predictions, provided that enough field data is available.
PB Sprnger
SN 2190-5487
YR 2025
FD 2025-07-18
LK https://hdl.handle.net/20.500.14352/123031
UL https://hdl.handle.net/20.500.14352/123031
LA eng
NO Martínez-Santos, Pedro, et al. «A Surrogate Approach to Model Groundwater Level in Time and Space Based on Tree Regressors». Applied Water Science, vol. 15, n.o 8, agosto de 2025, p. 206. DOI.org (Crossref), https://doi.org/10.1007/s13201-025-02572-5.
NO Ministerio de Ciencia e Innovaión
NO European Commission
DS Docta Complutense
RD 19 mar 2026