Dam Deformation Forecasting of Leapfrog Combined Model Merging Residual Errors of Chaos

There are advantages inanalysis of theintrinsic chaotic component in the fitting residuals in displacement monitoring statistics as well as in traditional algorithms for mining dam monitoring information. This paper therefore combines the characteristics of the conventional optimization algorithm, based on using frog leaping algorithm (SFLA) to determine the optimum weight in the sub-model, to establisha dam displacement combination monitoring model based on SFLA. Taking the chaotic characteristics of fit residuals in the statistical analysis into account through using phase space reconstruction and chaos theory, we analyzed displacement residuals and predicted values, and superimposed the forecast residual term with SFLA model predictions and developed leapfrog algorithm combination forecasting methods fusing chaos residuals, and a dam displacement leapfrog algorithm implementation process that considers of the chaotic residuals. Examples show that the forecasting ability of this model provides a new, improved approach to the analysis of dam deformation.