|This research aims at optimizing the monitoring network used to consistently identify pollution’s origin in the pollution source identification problem in groundwater hydraulics under real-time/operational applications. For this task, Machine Learning (ML) and Deep Learning (DL) methods are introduced, which can outperform metaheuristics, such as Genetic Algorithms (GAs), in terms of total computational load. To test the approach, a theoretical aquifer with two pumping wells is studied, where one of six possible pollution sources may spread a conservative pollutant. An existing own software simulates a 2D surrogate steady state flow field, using particle tracking to simulate advective mass transport only. A large number of combinations of possible source locations (4 different layout scenarios), hydraulic gradients and pumping wells’ flow-rates is used to calculate various features (such as pollutant arrival times, hydraulic drawdowns) in a 29x29 grid. Three ML/DL methods (Random Forests, Multi-Layer Perceptron, Convolutional Neural Networks) are tested for prediction accuracy, while Correlation based Feature Selection (CFS), and targeted tests are used to select subsets/sampling frequencies that can provide similar accuracy with the full datasets. This evaluation process bears promising results and paves the way for monitoring network optimization.|
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