Modeling the warning system of tour sustainable development with gravitational search algorithm support vector regression

In this paper, a warning system is constructed using Gravitational Search Algorithm Support Vector Regression (GSA-SVR). The gravitational search algorithm (GSA) is used to optimize the regularization parameter of Support Vector Regression (SVR) and is compared to particle swarm optimization. First, the history data of each index are normalized to (0,1). Then, the weights of each index are determined by using grey relationship theory, and meanwhile, the degrees of sustainable development of each year are calculated. The sustainable development of each year is used as sample to train the SVR. The trained SVR is utilized as the warning model to predict the degree of sustainable development in future years. The proposed method is applied to the early warning of tour sustainable development of Qinhuangdao. The simulation results show the effectiveness of the proposed method.