STARS4Water partner UCM presented on ‘A machine learning application for the development of groundwater vulnerability studies’. This research aims to improve upon the classic DRASTIC approach by combining what is actually known about groundwater contamination in a given aquifer with artificial intelligence approaches. A large number of machine learning algorithms from different families was trained on groundwater monitoring data for a series of aquifers in central Spain. This served the purpose of identifying which of the DRASTIC explanatory variables (depth to the water table, recharge, aquifer media, soil type, topography, impact of the vadose zone, hydraulic conductivity, land use) were more relevant in each part of the study region. Overall, tree-based algorithms are observed to outperform other supervised classification approaches on a regular basis. Certain ensemble methods are also adept at depicting groundwater vulnerability. This approach is versatile enough to cater to other classic vulnerability methods and can be readily exported to other settings.

Reference: V. Gomez-Escalonilla; P. Martinez-Santos; A. De La Hera-Portillo; S. Diaz-Alcaide; E. Montero Gonzalez; M. Martin-Loeches: A machine learning application for the development of groundwater vulnerability studies, 15^th International Conference on Hydroinformatics, HIC 2024, Beijing, China, 27-31 May 2024.

STARS4Water partner UCM presented on ‘GIS-based machine learning applications as decision support systems to enhance groundwater monitoring networks’. This research presents an approach to underpin the design of groundwater quality monitoring networks based on the application of multiple supervised classification algorithms. The method is illustrated through its application to a series of aquifer systems in central Spain. Classifiers were trained on a sample of borehole data to identify which spatially-distributed variables explain the presence of selected contaminants in groundwater. Spatially-distributed explanatory variables included slope, thickness of the unsaturated zone, lithology, and land use, among others. This method provides an alternative to expert-based criteria as to where to site new groundwater monitoring stations and can be readily exported to other settings. Tree-based classifiers such as random forest and extra trees proved the most accurate predictors of groundwater contamination, rendering predictive and AUC scores in excess of 0.8.

Reference: V. Gomez-Escalonilla; P. Martinez-Santos; S. Diaz-Alcaide; E. Montero Gonzalez; M. Martin-Loeches: GIS-based machine learning applications as decision support systems to enhance groundwater monitoring networks, 15^th International Conference on Hydroinformatics, HIC 2024, Beijing, China, 27-31 May 2024.