**4. Conclusions**

GWL is a crucial indicator for evaluating the health of groundwater resources in the Hebei Plain. This study attempted to model the GWL in the Hebei Plain and predict dynamic changes in the GWL using SVM, LSTM, MLP, and GRU models, and it qualitatively and quantitatively analyzed the training and testing datasets in the modeling process. The main conclusions were as follows:


**Author Contributions:** Conceptualization, Z.W. and C.W.; methodology, Z.W.; software, C.L.; validation, Q.S., W.L. and X.H.; investigation, Z.W., C.W. and T.Q.; resources, Q.S.; data curation, C.W.; writing—original draft preparation, Q.S. and Z.W.; writing—review and editing, C.W.; visualization, Q.S.; supervision, W.L.; project administration, L.Y.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key Research and Development Program of China (2021YFC3000205), Heilongjiang Provincial Applied Technology Research and Development Program (GA19C005), Key R & D Program of Heilongjiang Province (JD22B001), and Independent Research Project of the State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (SKL2022ZD02).

**Data Availability Statement:** The data will be available on request.

**Acknowledgments:** The authors would like to thank the editor and two anonymous reviewers for taking the time to provide their helpful feedback and suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**


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