Open AccessDam Deformation Prediction Based on Modified Grey Wolf Optimization Algorithm and Support Vector Machine
Author(s) -
Mingjun Li,
Junxing Wang,
Pan Jiangyang,
Peng Cheng,
Li Songzhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012084
Subject(s) - support vector machine , crossover , algorithm , kernel (algebra) , differential evolution , deformation (meteorology) , deformation monitoring , computer science , genetic algorithm , population , data mining , artificial intelligence , machine learning , mathematics , geology , oceanography , demography , combinatorics , sociology
The establishing of an accurate and reliable dam deformation prediction model is an important content of dam safety evaluation. To this end, a dam deformation prediction method based on support vector machine (SVM) with optimal parameters selected by improved grey wolf optimization algorithm (IGWO) is proposed by introducing the crossover and mutation operators of the differential evolution algorithm into the GWO algorithm. The initial population is enriched by the differential evolution algorithm, an IGWO is proposed, and the penalty factor and kernel function of SVM are optimized by the IGWO algorithm, then a dam deformation prediction model based on the SVM-IGWO algorithm is established. The results will be compared with those of the SVM and SVM-GWO models through the measured data of Xiluodu super high arch dam. The research shows that the proposed SVM-IGWO model can effectively improve the accuracy of dam deformation prediction.