Research on a Seepage Monitoring Model of a High Core Rockfill Dam Based on Machine Learning

The seepage of a rockfill dam with a high core wall is an important and difficult issue in the safety monitoring of a core rockfill dam, something about which managers are immensely concerned. Seepage of a high core rockfill dam is mainly affected by factors such as water level, rainfall, temperature, filling height, and aging. The traditional research method is to establish a multiple linear regression model to analyze the influence factors of seepage. However, the multicollinearity between these factors affects parameter estimation, and random errors in the data cause the regression model to fail to be established. This paper starts with data collected by an osmometer, uses the 3 delta criterion to process the outliers in the sample data, uses the R language to perform principal component analysis on the processed data to eliminate the multicollinearity of the factors, and finally uses multiple linear regression to model and analyze the data. Taking the Nuozhadu high core rockfill dam as an example, the influencing factors of seepage in the construction period and the impoundment period were studied and the seepage was then forecasted. This method provides guidance for further studies of the same type of dam seepage monitoring model.