Predicting regional-scale groundwater levels at high spatial resolution using spatial Random Forest models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F25%3A00644779" target="_blank" >RIV/86652079:_____/25:00644779 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1569843225005655?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1569843225005655?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jag.2025.104918" target="_blank" >10.1016/j.jag.2025.104918</a>

Result language

angličtina

Original language name

Predicting regional-scale groundwater levels at high spatial resolution using spatial Random Forest models

Original language description

Groundwater is an important resource for sustainable crop growth and agricultural productivity, and groundwater level (GWL) fluctuations directly influence agroecosystems. However, GWL data for most regions is unavailable because of spatial scarcity and discontinuous groundwater observations. In recent studies, machine learning techniques have proven to be a useful tool for producing GWL estimates, but these techniques may not necessarily take into account distances and observations from the nearest points to unmonitored sites as covariates. In this study, we explored the potential of these additional spatial covariates by implementing Random Forest for Spatial Interpolation (RFSI) to generate high-resolution (1 km2) GWL estimates on a monthly scale. To evaluate the effectiveness of the RFSI model, we performed a thorough comparison of the RFSI model with a conventional Random Forest (RF) model, a Random Forest for spatial data (RFsp) and a Support Vector Machine (SVM). The framework was implemented using GWL observation data for the period 2001 to 2022 in Brandenburg, Germany. The RFSI model exhibited the highest predictive accuracy for GWL predictions at testing points, achieving an R2 value of 0.86, surpassing the performance of RF, RFsp and SVM with R2 values of 0.75, 0.72 and 0.67, respectively. Incorporating nearby-well information substantially improved 1 km GWL predictions: RFSI achieved the lowest RMSE (3.77 m), as compared to RF (4.85 m), RFsp (5.04 m), and SVM (5.59 m). Notably, among the models tested, RF, RFsp, and SVM showed notable inaccuracies, substantially underestimating groundwater levels (GWL) in deeper wells (>10 m) and overestimating levels in shallow wells (1–10 m). In contrast, RFSI performed better, with smaller overestimations (up to ∼1.1 m) and less severe underestimations (up to ∼−3.9 m). Furthermore, the RFSI model demonstrated significantly higher computational efficiency compared to RFsp and SVM, particularly for large training datasets and high-resolution prediction mapping

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10509 - Meteorology and atmospheric sciences

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Applied Earth Observation and Geoinformation

ISSN

1569-8432

e-ISSN

1872-826X

Volume of the periodical

144

Issue of the periodical within the volume

NOV.

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

19

Pages from-to

104918

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105019926690