Tourism impact assessment modeling of vegetation density for protected areas using data mining techniques

In protected areas (PAs), the lack of tourism impact prediction models of vegetation is a shortcoming in PA management. Now, the main question are how recovery can be accelerated, or which ecological factors are associated with the rehabilitation of vegetation density? We aimed to compare the multilayer perceptron (MLP), radial basis function neural network (RBFNN), and support vector machine (SVM) models to predict tourism impact on land vegetation density changes. Three old national parks in Iran with diversity in tourist pressure and ecological condition were selected for analysis. We recorded 12 ecological and tourist variables in 400 sample plots, which are classified by topography, plot soil, and tourist pressure factors. We developed the tourism impact assessment model (TIAM) by MLP, RBFNN, and SVM techniques. Comparing with RBFNN and SVM, the MLP model (TIAM(MLP)) is introduced as the most accurate model for vegetation density changes for tourism impact assessment in PAs. The MLP model represents the highest value of R-2 in training (.969), test (.806), and all datasets (.876). Sensitivity analysis proved that the values of the tourist pressure, soil organic matters, soil moisture, soil porosity, and soil electrical conductivity are respectively as the most significant inputs, which influence TIAM(MLP) in PAs. We concluded that habitats with higher organic matter and moisture in the soil would likely tolerate more tourists' pressure. The MLP model, as a tool for PAs managers, is able to predict vegetation density changes under tourism pressure precisely.