A combined optimization prediction model for earth-rock dam seepage pressure using multi-machine learning fusion with decomposition data-driven

Abstract

Seepage pressure data analysis is considered one of the most direct ways to evaluate seepage safety in dam engineering. Therefore, accurate and consistent prediction of seepage pressure is essential. However, satisfactory predictive performance is rugged due to the irregular, nonlinear, and non-stationary nature of seepage pressure monitoring data. This paper proposes a combined optimization prediction model for seepage pressure prediction to resolve the deficiency. The model is coupled with a data decomposition technology, multi-machine learning fusion technology, adaptive variable weight combined strategy, and an improved optimization algorithm. The interaction between high and low frequency components is eliminated by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique to achieve the smoothing of non-stationary data. Multi-machine learning models are integrated by the combined strategy to compensate for the shortcomings of single models. Combined weights are adaptively changed by the kernel extreme learning machine (KELM) variable weighting strategy to overcome the limitations of traditional fixed weights. For the challenge of determining KELM parameters, the artificial electric field algorithm is improved to optimize KELM parameters to accelerate the determination of adaptive weights. The application of the proposed model to an earth-rock dam demonstrates enhanced predictive accuracy of seepage pressure by implementing strategies. The model is an effective tool for the seepage pressure prediction of dams and is conducive to dam operation and maintenance management.