A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

Abstract

Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear. Oppose to a single-kernel extreme learning machine, which suffers from poor generalization performance and instability, in this study, we proposed an improved bat algorithm for dam deformation prediction based on a hybrid-kernel extreme learning machine. To improve the learning ability of the global kernel and the generalization ability of the local kernel, we combined the global kernel function (polynomial kernel function) and local kernel function (Gaussian kernel function). Moreover, a Lévy flight bat optimization algorithm (LBA) was proposed to overcome the shortages of bat algorithms. The results showed that our model outperformed other models. This proves that our proposed algorithm and methods can be used in dam deformation monitoring and prediction in different projects and regions. HIGHLIGHTS The Lévy flight was improved bat algorithm to overcome the shortcoming about speed and effectiveness of bat algorithm.; We integrated the advantage of Gaussian and polynomial functions as the kernel function of KELM (PGKELM).; The LBA-PGKELM at the top of LBA-SVM and BPNN which based on gradient descent method.;

Keywords

• bat algorithm
• dam deformation predation
• kernel extreme learning machine
• lishan reservoir
• mixed kernel