Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planning

Document Type : Research article

Authors

Associate Prof., Department of Forestry, Faculty of Natural Resources, Tarbiat Modares University, Noor, I.R. Iran

Abstract

This study presents landslide susceptibility (LS) prediction model using the Adaptive Neuro Fuzzy Inference System (ANFIS) and Geographic Information System (GIS) which incorporates the physiographic information. Such models are is useful for forest road planning. To this aim, a set of factors including the terrain slope, aspect, geology formation, curvature, distance to rivers, and distance to faults at occurred landslide points were integrated into the ANFIS model. The modeling using a subtractive clustering method returned a coefficient of determination (R2) of 0.73 and a root mean square error (RMSE) of 0.27 for the best model. The sensitivity analysis indicated the distance to the rivers, geology formation, terrain slope, curvature, distance to the faults, and aspect as the most effective factors on the landslide occurrence. Furthermore, an evaluation of existing roads on simulated LS map showed that the majority of the currently existing roads are located on “medium” and “high” LS classes.

Keywords

References

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Iranian Journal of Forest and Poplar Research
Volume 22, Issue 3 - Serial Number 3
October 2014
Pages 509-526

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