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This article discusses the use of machine learning methods for the optimal design of groundwater circulation wells (GCWs). Traditional numerical simulation methods are limited in their ability to efficiently and comprehensively optimize GCW design. The study introduces a new approach using machine learning models to improve optimization speed and expand the scope of parameter optimization. The models were trained and evaluated using multiple linear regression, artificial neural networks, and support vector machines. The results showed notable correlations between anticipated outcomes and datasets, and the optimal design was successfully applied to a site in Xi’an.
