Machine learning as a tool to improve groundwater monitoring networks
Machine learning techniques are gaining recognition as tools to underpin water resources management. Applications range widely, from groundwater potential mapping to the calibration of groundwater models. This research applies machine learning techniques to map and predict nitrate contamination across a large multilayer aquifer in central Spain. The overall intent is to use the results to improve the groundwater monitoring network. Twenty supervised classifiers of different families were trained and tested on a dataset of fifteen explanatory variables and approximately two thousand points.