Using logistic regression and random forest techniques to predict the degradation of forest roads

Abstract

Abstract The destruction of forest roads has significant adverse impacts on forest accessibility, resulting in heightened maintenance and environmental expenses, as well as posing potential threats to human life. Enhancing the sustainable and secure accessibility to forest regions necessitates a more comprehensive knowledge of the deterioration of forest pathways across temporal and spatial dimensions. The objective of this study is to utilize destruction prediction models to identify pertinent variables related to the state of the Hyrcanian forest road network in Golestan province. Additionally, the study aims to determine the most significant explanatory variables. To achieve the research objective, a set of 50 points were established along forest roads at a uniform distance of 200 meters from each other. The response variable, which pertains to the visibility of the forest road infrastructure, was recorded in binary format. Additionally, the explanatory variables were documented along a strip transect with a width of 10 meters perpendicular to the road. The study employed two conventional logistic regression models and a novel random forest model to forecast the deterioration of forest roads. Three indicators consist of TWI, natural ground gradient and cover density of cut slope, can predict road damage, as demonstrated by the implementation of two models. The random forest model exhibited superior accuracy to logistic regression, as evidenced by its success rate of 0.73 and prediction rate of 0.68. The results indicate that machine learning models offer significant insights into predicting road conditions and ensuring access to the Hyrcanian forests.