Improved extreme learning machine-based dam deformation prediction considering the physical and hysteresis characteristics of the deformation sequence

Accurate modeling and prediction of dam deformation contribute to the analysis of dam safety. Research on data-driven modeling of dam deformation has received increasing attention. However, most established models cannot comprehensively consider the physical characteristics (such as irreversible trend changes, periodic changes, and random changes) and hysteresis characteristics of the deformation sequence. Therefore, an improved extreme learning machine-based dam deformation prediction model was proposed. The parameters of the extreme learning machine were optimized using the improved salp swarm algorithm. For identifying physical characteristics, seasonal and Trend decomposition using Loess was used to decompose the deformation sequence into trend, periodic, and residual items. For the identification of hysteresis characteristics, the phase space reconstruction theory was adopted to solve the problem of selecting the time lag of the deformation subsequence with chaotic characteristics. The proposed model was used to predict the deformation of a concrete dam in China. Furthermore, the model outperformed other alternatives, thus providing a new solution for dam deformation prediction.