The Ecological Tourism Research Intotaibai Mountain Based On BP Network

Common forecasting method of travel demand is based on the mathematical model:statistical time series prediction method and causal model prediction. However, the tourismmarket is often constrained by many factors, showing a complex relationship. And there areunstable factors as well. The traditional method is difficult to obtain valid predictions.Artificial neural network is based on the mathematical model which imitates thenetwork structure and function of the brain. It can reveal the non-linear relationship containedin the data sample, and it is also an adaptive dynamic system composed by a large number ofprocessing units. It has a good adaptivity, self-organization and a strong capability of learning,association, fault tolerance and anti-jamming. It can make a model of the complex systemflexibly and conveniently, making it a good classification and prediction tools in practicalapplications. In general, the tourism market is also affected by many unpredictablefactors,therefore, a neural network is a superior model analysis methods when forecasting thetravel demand. The thesis analyzes and studies the basic theory and the forecasting methodsof artificial neural network. Meanwhile, it makes a preliminary study of the travel demandforecasting index, the choice of the neural network prediction model, the modeling processand the implementation method.The thesis makes an actual prediction analysis of Taibai Mountain Nature ReserveEcotourism demand by using this system. The results confirm the effectiveness of theforecasting system. In the process of forecasting, the2-8-1neural network structure is used topredict the number of tourists to Taibai Mountain Nature Reserve from2011to2020. Thepredicted data will be compared with the actual data to show that the the effect of the model isgood, and it can also ensure the good generalization ability of the network.