monitoring networks

A groundwater monitoring network surrounding a pumping well (such as a public water supply) allows for early contaminant detection and mitigation where possible contaminant source locations are often unknown. This numerical study investigates how the contaminant detection probability of a hypothetical sentinel-well monitoring network consisting of one to four monitoring wells is affected by aquifer spatial heterogeneity and dispersion characteristics, where the contaminant source location is randomized. This is achieved through a stochastic framework using a Monte Carlo approach.

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.