Research On The Construction Of Early-warning System Of Environmental Carrying Capacity In Forest Scenic Areas

It has become an urgent need for the academia to consider how to achieve advanced warning of tourism environmental carrying capacity for forest tourism scenic spots in the context of the flourishing development of China’s forest tourism,espeically the increasing number of visitors and the vulnerability of the ecological environment system in forest tourism scenic spots.Some problems have been discovered through the analysis of the re-search status quo of the carrying capacity of the tourism environment and early warning.On the one hand,the calculation of tourism environmental carrying capacity mostly based on "short board theo-ry" or "barrel principle" generally takes the largest or most suitable scale of tourists for the final evaluation result.However,this may not reflect the complexity and variability of the tourism environmen-t system overally.On the other hand,the research field of early w-arning of the carrying capacity of tourism environment focus on to-urism cities,tourist islands and other tourist destinations is narrow,and the result tends to comprehensively monitor bearing capacity of early warning.Above all,this paper intends to adopt the indicatorbased assessment method for the carrying capacity of tourism environment and construct a research on the early-warning system of the environmental carrying capacity of forest tourism scenic spots based on a certain warning theory.However,the forest tourism area is different from the general area for complex of the tourism environment system and the difficulty of the the establishment of an accurate model.And BP neural networks used widely in other disciplines and industry fields can meet this demand.Therefore,this study intends to introduce the BP neural network technology for the construction of an early warning system for the tourism environmental carrying capacity of forest tourist attractions,and to verify its feasibility and effectiveness through an empirical case—Gypsum Mountain Scenic Area.First of all,this study constructed a set of universal early warning assessment indicators for tourism environment carrying capacity based on the unique characteristics of forest tourism scenic spots based on the introduction of relevant theoretical foundations.Secondly,this study offer a detailed introduction to the methods and modules for the construction of early warning systems.At last,this study conducted an empirical stud-y.And the warning result shows that,it need to be taken seriously on account of the economic environment bearing capacity early war-ning state of gypsum mountain scenic spot has been in transition t-o "appropriate area".The natural ecological environment carrying c-apacity,the social environment bearing capacity and the overall tou-rism environment carrying capacity early warning status are still in the "healthy area",it’s just to be paid little attention.This study verifies the feasibility of using BP neural network technology to construct an early warning system for environmental carryi-ng capacity of forest scenic spots and the reality of realizing dynamic early warning of the carrying capacity of tourism environment,and the result shows that the method is of feasibility and effectiven ess,and it can realize the dynamic early warning simulation of the tourism environment carrying status of the scenic spot at present an-d during a period of time,and realize dynamic early warning evaluation on time series.However,given the lack of research techniqu-es and tools,this study only considers the overall tourism environment of the scenic spot,and pay attention to the changes in the timing of the state of the tourism environment bearing early warning.So in the follow-up study,it is possible to study different time spo-ts in the study area to highlight the spatial-temporal differentiation of the tourist environment in the scenic spot.