## 6 Conclusions  
This study employs the GAIN algorithm to fill in missing TWSA data and calculate the FPI to predict flood events. The results demonstrate that the use of GAIN successfully reconstructs the missing values in TWSA, enabling a more comprehensive temporal analysis of hydrological variations. FPI results derived from the reconstructed TWSA effectively capture major flood events, showing good temporal correlation with observed precipitation data.  
The study period reveals several notable flood years, specifically 2005, 2006, 2008, and 2022. The results indicate that the GAIN-reconstructed TWSA-based FPI can reasonably characterize the magnitude and timing of flood events, though minor discrepancies are observed in terms of underestimating peak flood potential during extreme precipitation periods.  
However, despite the promising performance, there are several potential avenues for improvement. The current model only utilizes the FPI index as a feature for prediction. The inclusion of additional features, such as soil type or other environmental variables, may improve the model's predictive accuracy. Despite this, the study provides evidence that GAIN-facilitated TWSA reconstruction is a promising approach to improve flood prediction, particularly in regions with limited data availability. The findings underscore the importance of using advanced machine learning techniques to address data gaps and enhance the accuracy of hydrological forecasts, which is crucial for flood risk management and mitigation.  
Conceptualization, J.W. and H.L.; methodology, [PERSON] and S.W.; software, G.N.; validation, [PERSON], H.L. and Y.W.; formal analysis, S.W. and G.N.; investigation, J.W.; resources, H.L. and Y.W.; data curation, J.W.; writing--original draft preparation, [PERSON] and Y.W.; writing--reviewand editing, H.L. and S.W.; visualization, J.W.; supervision, H.L. and S.W.; project administration, H.L.; funding acquisition, H.L. and S.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (Grant No. 42404017), Scientific Research Starting Foundation of Xidian University (grant number 20103237789), Scientific Research Starting Foundation of Xidian University Guangzhou Institute of Technology (grant number 01151601), and Hunan Provincial Natural Science Foundation of China (Grant No. 2024J8377).  
The data presented in this study are available on request from the corresponding authors.  
The authors would like to express their gratitude to the Jiangsu Provincial Hydraulic Research Institute for providing methodological guidance and to [PERSON] for his valuable corrections and feedback on the methods used in this study. We would also like to thank the Jet Propulsion Laboratory (JPL, [[https://podaac.jpl.nasa.gov](https://podaac.jpl.nasa.gov)]([https://podaac.jpl.nasa.gov](https://podaac.jpl.nasa.gov)), accessed on 16 December 2024), Center for Space Research at the University of Texas at Austin (CSR, [[http://www2.csr.utexas.edu/grace](http://www2.csr.utexas.edu/grace)]([http://www2.csr.utexas.edu/grace](http://www2.csr.utexas.edu/grace)), accessed on 16 December 2024), and NASA's Goddard Space Flight Center (GSFC, [[https://earth.gsfc.nasa.gov](https://earth.gsfc.nasa.gov)]([https://earth.gsfc.nasa.gov](https://earth.gsfc.nasa.gov)), accessed on 16 December 2024) for providing their GRACE/FO mason solutions. We thank NASA's Integrated Multi-satellite Retrievals for GPM (IMERG, [[https://gpm.nasa.gov/data/imerg](https://gpm.nasa.gov/data/imerg)]([https://gpm.nasa.gov/data/imerg](https://gpm.nasa.gov/data/imerg)), accessed on 16 December 2024) for providing precipitation data. We also thank the International Disaster Database Center for Research on the Epidemiology of Disasters (EM-DAT) for sharing the flood hazard report.  
The authors declare no conflict of interest.