An improved radial basis function neural network for displacement prediction of a reservoir slope

Abstract

Landslide prediction necessitates viewing the past, present, and future states of a slope as a constantly changing dialectical unity, with prediction laws derived from known past and present information. Through in-depth analysis of the structure and training methods of radial basis function (RBF) neural networks, an optimization method of RBF network diffusion velocity function based on the particle swarm optimization (PSO) algorithm was introduced in this study, aiming at the problem of limited coverage of spread value range determined by the empirical value or trial calculation method, so as to realize the large-scale and efficient search of RBF network diffusion function. To address the problem that the prediction accuracy of the data-driven model based on displacement increment sequences built by RBF intelligent algorithm is difficult to be guaranteed when the displacement increment mutation point exists, the PSO-RBF intelligent coupling model based on gray system theory pre-processing is constructed to improve the prediction accuracy of the model from the perspective of improving the prediction accuracy of displacement increment mutation points. Taking the data from ZG88 monitoring point of Shuping landslide as a case study, the slope displacement prediction analysis is carried out. The results demonstrate that the optimization method for RBF network diffusion velocity parameters based on PSO can efficiently and accurately identify the global optimal value within the range of 0–1,000. The computation process takes approximately 13 min, significantly enhancing the calculation efficiency. The RBF mixed model, incorporating gray system theory, leverages the valuable information extracted from prior calculations of the GM(1,1) model group. This integration enhances prediction accuracy compared with that achieved by the singular PSO-RBF method. The developed algorithms and research results may be expected to be applied in practical engineering.