Abstract
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 29 × 29 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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Acknowledgments
This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Re-sources Development, Education and Lifelong Learning 2014-20» in the context of the project “Evolution of Computational Intelligence in Environmental Engineering-Generalization, Improvement, Optimal Combination of Methodologies in Air Quality & Water Resources Problems” (MIS 5052163).
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Kontos, Y.N., Kassandros, T., Katsifarakis, K.L., Karatzas, K. (2021). Deep Learning Modeling of Groundwater Pollution Sources. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_14
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