Modeling Of Sustainable Development For Qinhuangdao Tourism

Tourism is an important industry in the national economy of Qinhuangdao, and its healthy and sustainable development are key components of promoting good and quick development of the national economy. In recent years, with the increasing external demand for tourism, the development of tourism in Qinhuangdao is very fast. This has resulted in the development of many tourism resources and even over-exploitation in some areas. With the increase of passengers, surrounding environment of the scenic spot has been damaged with different degrees. More and more pressure has been put on the social infrastructure(such as transportation, accommodation, etc.) and social resources, which have affected the healthy and sustainable development of tourism in Qinhuangdao. So the construction of the early warning system for the sustainable development of tourism in Qinhuangdao is of great significance, from which we can forecast the future development of tourism in Qinhuangdao. We can also present effective measures according to the forecasting results about the spatiotemporal range and damage degree of abnormal state.In this paper, the modeling approach is based on the support vector machine. Because the early warning model we established is a regression model, we choose the support vector regression method. For the reason that the selection of super parameter in support vector regression makes a great difference to the modeling results, the gravitation optimization algorithm is used to optimize the parameters. The GSA-SVR algorithm which has a better result than the traditional grid search method is proposed in this paper. As a result, we can get better modeling results.Because sustainable development of tourism in Qinghuangdao is a complex process and it involves many fields, so we classify and summarize reasonable pre-warning indexes and set up a clear early warning index system. According to the characteristics of Qinhuangdao, we set four representative kinds of primary indexes, including the development index of tour economy, the support index of ecological environment, and the development ability index of tourist market. And each primary index consists of certain number of secondary index. Based on the index system, we establish an early warning model for the sustainable development of tourism in Qinhuangdao.Finally, considering the 2005~2012 development data of tourism in Qinhuangdao, BP neural network and SVR are used to compare with our results from GSA-SVR algorithm in this paper. Comparing results verify that the proposed GSA-SVR algorithm is better. Furthermore, by means of the analysis of the experiment, we can deduce that sustainable development of the tourism has been paid a lot attention to in Qinhuangdao. The study result of statistics in recent years has also shown that the sustainable development of tourism in Qinhuangdao is on-going with a strong degree.