GIS-based spatial prediction of recreational trail susceptibility in protected area of Sikkim Himalaya using logistic regression, decision tree and random forest model

Abstract

Trails have high conservation value which provides access to the protected area. But expansion of recreational activities along the trail has notably disturbed its environmental quality. The rapid increase of recreational activities along trails of Sikkim Himalayan region has become a major environmental concern. Therefore, modelling and mapping of sensitive trails are essential aspects for decision makers. The present study integrates RS-GIS with different machine learning algorithms to prepare trail susceptibility mapping. Furthermore, the study compares the predictive performance of logistic regression (LR), decision tree (DT) and random forest (RF) model for trail susceptibility mapping. Here we have considered seventeen trail susceptibility conditioning factors as model input. Thereafter, the dataset was randomly divided into two parts: training dataset (70%) and validation dataset (30%). Multicollinearity analysis carried using variance inflation factor (VIF) and tolerance (TOL) to reduce model biasness. Thereafter, trail susceptibility map prepared using LR, DT and RF models. Finally, Receiver operating curve (ROC)- area under curve (AUC) method, statistical overall accuracy (OA) and Kappa index were used to measure the predictive performance of the models. The study concluded that LR (AUC-0.948, OA-94.8% and Kappa Index- 0.897) gives better performance in overall accuracy assessment as compared to DT (AUC- 0.931, OA- 93% and Kappa Index- 0.862) and RF (AUC- 0.914, OA- 91.3% and Kappa Index- 0.828) model.