Abstract
This paper presents an innovative approach to the prognostic modelling of piezometric levels in earthen dams equipped with automated monitoring systems. The main idea of the approach and the expected prognostic results are illustrated with the example of prediction of piezometric levels in the earthen dam of the Kyiv hydropower plant. This usually complex prediction task is simplified in this approach by means of simple regression models and combined situational and inductive modelling which enables overcoming the excessive uncertainty in time series. To calibrate the interpretation and prognostic models, daily monitoring data of the piezometric levels over a period of eight years was used. To verify the prediction results, monitoring data collected in the three years following this eight-year period was used. The goodness of fit of interpretation models was performed by R2 testing. To assess the goodness of the prediction fit, mean absolute and relative error estimators, as well as the Nash-Sutcliffe efficiency coefficient, were employed.
Highlights
Dams, which are widely used in various economic sectors and spheres of human life, are among the most typical objects relating to the hazard potential structures
The combination of situational and inductive modelling is implemented in the approach
Our study shows that by having complete monitoring data sets it is easier to provide monotony and homogeneity of data for short time intervals, and to optimise models through the elimination of unimportant factors
Summary
Dams, which are widely used in various economic sectors and spheres of human life, are among the most typical objects relating to the hazard potential structures. The widespread use of earthen dams considering the devastating accidents that have occurred in the past [3, 4], makes the problem of the reliable operation of dams important. Pre-modelling may result in the removal of some predictors in order to facilitate the simplification of the model structure. First of all, it is about the removal of intercorrelated predictors. All considered simplifications should be justified in terms of predictive validity
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have