Research on Dam Deformation Prediction Model Based on Optimized SVM

Although constructing a dam can bring significant economic and social benefits to a region, it can be catastrophic for the population living downstream when it breaks. Given the dynamic and nonlinear characteristics of dam deformation, the traditional dam prediction model has been unable to meet the actual engineering demands. Consequently, this paper advocates for a novel method to solve this issue. The proposed method is based on the optimization of improved chicken swarm (ICSO) and support vector machine (SVM). To begin with, the mean square error is used as the objective function, and then, we apply the improved chicken swarm algorithm to iterate continuously, and finally, the optimal SVM parameters are obtained. Through the modeling and simulation experiments of a nonlinear system, the validity of the improved chicken swarm algorithm to optimize an SVM model has been verified. Based on the horizontal displacement monitoring data of FengMan Dam, this paper analyzed the influencing factors of horizontal displacement. According to the results, three prediction models have been established, respectively: the SVM prediction model optimized by the improved chicken swarm algorithm, the SVM prediction model optimized by the basic chicken swarm algorithm and the BP neural network prediction model optimized by the genetic algorithm. The obtained results from the experiment authenticate the validity and superiority of the proposed method.