Groundwater level monitoring network design with machine learning methods

Abstract

This research introduces a method combining groundwater models and machine learning (ML) algorithms to locate observation wells and design optimal Groundwater Level Monitoring Networks (GLMNs). Groundwater models and stochastic simulations are used to extract required hydrogeological datasets for ML algorithms. In addition to data generation, the stochastic simulations minimize the uncertainties in the aquifer characterization, leading to a precise design of GLMNs. In this research, K-means clustering and relevance vector machine (RVM) are the ML algorithms employed to determine the optimal configuration of observation wells in terms of number and location in a monitoring network. This study proposes three GLMNs (K-mean, RVM, modified RVM), compares them with the existing observation wells, and investigates their effects on the accuracy of groundwater modeling and running time. The groundwater model with a K-mean network runs faster than other configurations, while the model with a modified RVM network shows a significant decrease in errors.