Approach to mapping Groundwater-Dependent Ecosystems through machine learning in Central Chile
Year: 2025
Type: article
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. The original MLMapper code is available in the repository of the Environmental Hydrogeology research group of the Universidad Complutense de Madrid (https://www.ucm.es/hidrogeologia/programas-software, Martínez-Santos and Renard, 2019) and is currently undergoing improvements to its user interface.
Authors: Duran Llacer, Iongel, Gómez-Escalonilla, Víctor, Aliaga, Marcelo, Arumi, Jose Luis, Zambrano, Francisco, Rodríguez López, Lien, Rebeca, Martínez Retureta, Martínez-Santos, Pedro
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
Awards: ASSESSMENT OF CURRENT AND FUTURE WATER AVAILABILITY FOR AGRICULTURE AND TERRESTRIAL ECOSYSTEMS UNDER DIFFERENT LAND-USE SCENARIOS OVER THE ACONCAGUA BASIN TOWARD DROUGHT ADAPTATION, G3338876941, ASSESSMENT OF WATER RESPONSE OF GROUNDWATER-DEPENDENT ECOSYSTEMS TO DROUGHT IN CENTRAL CHILE: A VIEW TOWARDS TO CLIMATE CHANGE ADAPTATION
OpenAlex ID: W6949804681
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