Abstract
After road construction in steep and mountainous areas, there is always a risk for trench failure. Estimation of this probability before forest road design and construction is urgent. Besides, to decrease failures costs and risks, it is necessary to classify their occurrence probabilities and identify the factors affecting them. The present study compares three statistical models of logistic regression, frequency ratio, and maximum entropy. The robust one was applied to generate trench failures susceptibility map of forest roads of two watersheds in Northern Iran. Also, all failures repairing costs were estimated, and subsequently, all existing roads were surveyed in the study area, detecting 844 failures. Among the recorded failures, 591 random cases (70%) were used in modeling, and others (30%) were used as validation data. The digital layers, including failure locations, were prepared. Three failure susceptibility maps were simulated using the outputs of the mentioned methods in the GIS environment. The resulted maps combined with repair cost prices were analyzed to statistically evaluate the repair cost unit per meter of forest road and per square meter of failure. The results showed that the logistic regression model had an Area Under Curve (AUC) of 74.6% in identifying failure-sensitive areas. The probabilistic frequency ratio and Entropy models showed 68.2 and 65.5% accuracy, respectively. Based on the logistic regression model, the distance to faults and terrain slope factors had the highest effects on forest road trenches failures. According to the result, about 43.25% of the existing road network is located in »high« and »very high« risky areas. The estimated cost of regulating and profiling trenches and ditches along the existing roads was approximately 108,772 $/km.
Highlights
Trenches Failures (TFs) are among the most significant geological hazards worldwide that lead to substantial economic and human losses (Del Ventisette et al 2012)
Some factor classes gained high values for all three models. These factors include the distance from faults, slope gradient (20–30°), elevation (500–800 m), lithology formation (Diorite-gabbro, gabbro volcanic rocks), and soil
The result of the spatial relationship between conditioning factors and TF locations revealed that the class of 0–200 m distance to faults had the highest values of 3.49, 0.281, and –0.256 in frequency ratio (FR), maximum entropy (Maxent), and logistic regression (LR) models, respectively
Summary
Trenches Failures (TFs) are among the most significant geological hazards worldwide that lead to substantial economic and human losses (Del Ventisette et al 2012). Assessing areas susceptible to TFs is of great importance to reduce and manage failure-related disasters. The assessment of failure susceptibility modeling has become one of the leading global research topics over recent years (Bhandary et al 2013). The costs of failure damage are proven to be of economic significance, their systematic estimation efforts are still rare. Evaluation of failure costs requires the consideration of complex causalities and high spatiotemporal variability.
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