Comment on “Synergistic identification of hydrogeological parameters and pollution source information for groundwater point and areal source contamination based on machine learning surrogate-artificial hummingbird algorithms” by Luo et al.

Luo et al. present an inversion framework that combines BPNN surrogate modeling with the AHA optimization algorithm for groundwater contamination source identification, and they comprehensively evaluate the performance of different surrogate models. The work is generally well written. However, several significant issues must be addressed to improve the clarity of the paper. The most critical concern lies in the structure of the Introduction. Although the authors provide an extensive literature review, the research gap and the novelty of this study in relation to previous work are not clearly emphasized. Secondly, the Discussion section lacks depth, which substantially weakens the novelty and the implications of this study. Finally, the language throughout the manuscript should be thoroughly revised and polished before publication.

 Specific comments:

Lines 127-135 The authors are recommended to reorganize the research objectives. The current unclear objectives obscure the novelty of the paper. This confusion is caused by an unclear summary of the research gap.

Line 151 MODFLOW and MT3DMS are not packages.

Line 305 Replace “inhomogeneous” by “Heterogeneous”

Lines 387-389 The authors are suggested to combine this sentence with the previous paragraph to create a clearer contrast, which would make the comparison more striking. Additionally, I am skeptical about the reported runtime for the 1000 iterations. Considering that the model in this study is at the field scale, consists of only a single model layer, and uses a rather coarse grid discretization, a runtime of 500 hours seems excessively long.

Lines 420-424 This section reads more like a repetition of the Introduction. It is recommended that the authors first present their own findings in the Discussion before comparing them with other studies. Additionally, emphasizing the implications of this study would greatly enhance the value of the paper.

Lines 438-440 Please specify the advantages more clearly.

Lines 483 Including the limitations is good. The authors are suggested to include limitations in a separate section.

Lines 501 The authors are encouraged to include more quantitative findings rather than just qualitative notifications.

While the manuscript addresses an important challenge in groundwater contamination source identification, its novelty is limited. The core contribution lies in introducing the Artificial Hummingbird Algorithm (AHA) into a simulation-optimization framework, which is not a fundamentally new algorithm nor specifically tailored to groundwater inverse problems. Furthermore, many techniques used—BPNN, Kriging, PSO, SSA—are already well-established in the literature.

Moreover, the reported simulation results show extremely high precision (e.g., R² > 0.999, MRE < 2%), which may suggest possible overfitting or idealized experimental setups. The study lacks rigorous testing of generalization under realistic uncertainty scenarios, such as sparse observations, complex geological heterogeneity, or parameter noise. Without such assessments, the practical robustness and transferability of the proposed framework remain questionable.

The paper would benefit from a deeper methodological insight into why AHA performs better in this specific problem context, rather than merely benchmarking its numerical results. The current framing gives the impression of "algorithm replacement" without substantive theoretical or application-driven innovation.