Abstract: The seepage monitoring model of rockfill dams is a key factor for quantitative analysis of seepage safety. Most of the traditional models adopt a statistical model or machine learning intelligent algorithm model separately, unable to effectively integrate the advantages of both. This paper presents an innovative integration of statistical models with multiple parallel intelligent algorithm prediction models in the framework of integrated learning, and uses the interpretability of statistical models and the high adaptability of fit of intelligent algorithms to improve the prediction accuracy of this integrated model. First, we fully consider the lag effect of seepage influence factors on the basis of the classical seepage statistical model, and improve the expression for the water level factor and the rainfall factor. Then, based on the integration principle of differential evolution adaptive Metropolis (DREAMZS), several advanced intelligent algorithms and improved statistical models in machine learning are integrated, and optimal weight coefficients are obtained for each model. Case analysis shows that in comparison with the single statistical model or the intelligent algorithm model, our integrated learning fusion model improves prediction accuracy significantly and can integrate effectively the advantages of a statistical model and multiple intelligent models, providing a new modeling method for dam seepage monitoring.

Key words: rockfill dam, monitoring model, seepage safety, prediction model, integrated learning