Modeling of hiking trail degradation using machine-learning techniques to find optimized recreational trails in an arid environment

The present study aims to use different machine-learning algorithms to map trail susceptibility and use it to find the best hiking trail between specified locations across the Sarigol National Park and Protected Area (SNPP), Iran based on the least cost path analysis. Furthermore, the study compares the predictive performance of Artificial Neural Network, Support Vector Regression, and Gene expression programming model for trail susceptibility mapping. We have considered nine trail susceptibility conditioning factors as model input, namely Land use coverages, Landform classes, Annual precipitation, NDVI, Soil types, LS-factor, Wind explosion index, Topographic witness index, and Elevation. The study concluded that ANN gives better performance in overall accuracy assessment as compared to GEP and SVM models. The importance of predictor variables as identified by the ANN model indicated that the LS factor, Soil types, NDVI, and Landform classes represented the highest level of significance attributed to the model. The study found that LCPA is an efficient tool to find the “lowest land degradation” to connect two locations of hiking trails. This suggested that park planners should consider potential land degradation locations to identify suitable hiking trails.