Missing values commonly exist in dam inflow flood discharge monitoring data, which hinders flood analysis, risk assessment and reservoir scheduling. Aiming at the problems of insufficient imputation accuracy and the difficulty in adaptive threshold selection of traditional Singular Value Decomposition (SVD) in flood discharge data with strong fluctuations and high noise, this study introduces a method for filling in missing dam inflow flood discharge based on Dam Monitoring Data Reconstruction Model (DSVD). The method constructs a non-repeating sequence monitoring matrix, introduces a hard singular value threshold for adaptive denoising, and completes time series data imputation combined with a weight optimization model, which effectively improves the imputation accuracy of strongly fluctuating flood discharge data. Taking the measured inflow flood discharge data of Jinjiaba Reservoir in Chongqing as the research object, this study systematically analyzes the influence of column-to-row ratio (Ra) and data missing rate on imputation performance, and conducts a comparative verification against other models. Experimental results indicate that the optimal Ra value is 6. The coefficient of determination (R2) stays above 0.830 within a missing rate range of 5–40%, showing strong robustness against data loss. Compared with other benchmark models, the method has the highest R2 (0.875) and the lowest Root Mean Square Error (RMSE, 7.771), exhibiting stronger adaptability to mountainous flood discharge data with steep rise and fall characteristics. The research findings provide a new method for the high-precision recovery of missing dam inflow flood discharge data and reliable data support for reservoir flood risk analysis and safe operation.
Chen et al. (Fri,) studied this question.