Multi-machine learning methods for rapid and synergistic inversion of groundwater contamination source, hydrogeologic parameter and boundary condition

The application of machine learning methods to the groundwater pollution inversion problem has become a hot research topic in recent years. However, applying machine learning methods to achieve synergistic and rapid identification of pollution source information, hydrogeological parameter, and boundary condition is much limited. This study proposed to use multi-machine learning methods, including: multilayer perceptron (MLP), kernel extremum learning machine, support vector machine (SVR), and back-propagation neural network, to directly establish the inverse mapping relationship between the outputs of the simulation model and the inputs, and to realize the synergistic identification of multiple variables to be identified. The recognition accuracies of different machine learning methods for different types of variables to be recognized were compared, and the methods with good inversion performance were combined. The results showed that the SVR method had excellent accuracy in identifying the hydraulic conductivity and specific head boundary. The MLP method had good accuracy in identifying the release intensity of the pollutant sources. Therefore, by combining SVR and MLP (SVR-MLP), SVR was used to construct an inverse mapping relationship identifying hydraulic conductivity coefficients and head-specific boundary values, and MLP was used to identify pollutant release intensities, thus having the synergistic identification of all three realized. Overall, SVR-MLP improved the overall inversion accuracy. In order to verify the reliability of the method, several sets of reference values were selected to assess the inversion performance of the method, and the average absolute percentage error of the identification results of the multiple sets was less than 4 %, which emphasized the stability and reliability of the inversion method. It can provide a reliable basis for groundwater pollution remediation and treatment.