关键词: 大坝安全监测;数据填补;预测算法;缺失值;

Abstract: Algorithms for early warning and forecasting of dam safety monitoring based on historical data has high requirements on the quality of datasets, and the datasets containing missing values will significantly reduce the accuracy of the algorithm results.In order to improve the quality of data, this paper proposed a missing value filling algorithm that integrates the multi-measurement point data correlation.Based on the correlation of the time scale of each measuring point, the time series that satisfies a certain degree of correlation was used as the input of the prediction model, and the iterative technique was introduced to realize the automatic filling of the missing values of multiple measuring points.In order to verify the applicability of the algorithm to different types of missing datasets in practical engineering, according to the measured data samples, a total of 12 different types of missing datasets were constructed at three levels of missing rate, the concentration degree, dispersion degree and experiments were carried out.The results showed that for different types of missing datasets, the RMSE mean value of the algorithm was more than 15% higher than that of the traditional filling algorithm, and the nMAPE mean value was more than 1% higher.

Key words: dam safety monitoring; data filling; forecasting algorithm; correlation; missing value;