Approach to mapping Groundwater-Dependent Ecosystems through machine learning in Central Chile

Intensive pumping can significantly impact the ecological integrity of groundwater-dependent ecosystems (GDEs). Identifying and mapping these ecosystems is essential for their effective management and conservation. This study presents a new probabilistic approach that uses machine learning techniques to predict the presence of GDEs zones in the Ligua and Petorca basins, central Chile.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-16
DOI
https://doi.org/10.5281/zenodo.15676032
Primary Topic
Neural Networks and Applications
Type
article
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article

Approach to mapping Groundwater-Dependent Ecosystems through machine learning in Central Chile

Zenodo (CERN European Organization for Nuclear Research)
Neural Networks and Applications
article

Approach to mapping Groundwater-Dependent Ecosystems through machine learning in Central Chile

article en

Abstract

Intensive pumping can significantly impact the ecological integrity of groundwater-dependent ecosystems (GDEs). Identifying and mapping these ecosystems is essential for their effective management and conservation. This study presents a new probabilistic approach that uses machine learning techniques to predict the presence of GDEs zones in the Ligua and Petorca basins, central Chile.

Zenodo (CERN European Organization for Nuclear Research)
Universidad Mayor (CL), Universidad Complutense de Madrid (ES), University of Concepción (CL), Universidad Católica de Temuco (CL), San Sebastián University (CL)
Life in Land
Openalex Percentile: Top 75%
Neural Networks and Applications
DOIOpen Access PDFOpenAlex
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