Synergistic identification of hydrogeological parameters and pollution source information for groundwater point and areal source contamination based on machine learning surrogate–artificial hummingbird algorithm

Abstract. Effectively remediating groundwater contamination relies on the precise determination of its sources. In recent years, a growing research focus has been placed on concurrently estimating hydrogeological characteristics and locating pollutant origins. However, the precise synergistic identification of point and areal contamination sources of groundwater and combined hydrogeological parameters has not been effectively solved. This study developed an inversion framework that integrates machine learning surrogates with the artificial hummingbird algorithm (AHA). The surrogate models approximating the simulation system were constructed using both backpropagation neural networks (BPNNs) and Kriging techniques. The AHA was then employed to solve the optimized model, and its performance was benchmarked against particle swarm optimization (PSO) and the sparrow search algorithm (SSA). The applicability of this inversion framework was assessed by application to point sources of contamination (PSC) and areal source contamination (ASC). The robustness of the framework was verified through application to scenarios with different noise levels. The results showed that the surrogate model constructed by the BPNN method provided estimates that were closer to those of the simulation model in comparison to the Kriging method. The coefficient of determination (R2) is 0.9994 and mean relative error (MARE) is 3.70 % in PSC, and the R2 is 0.9989 and MARE is 4.48 % in ASC. The performance of the AHA exceeded that of the PSO and the SSA. In PSC, the MARE of the identification result is 1.58 %. In ASC, the MARE of the identification result is 2.03 %, with the AHA able to rapidly and accurately identify the global optimum and improve the inversion efficiency. The proposed inversion framework was demonstrated to apply to both groundwater PSC and ASC problems with strong robustness, providing a reliable basis for groundwater pollution remediation and management.