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. 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.