Planning a single low risk forest road based on artificial neural network model of landslide susceptibility (case study: Kojour watershed)

Naghdi, R.; Abbasian, A.; Ghajar, I.

doi:10.22059/jfwp.2017.212553.767

Planning a single low risk forest road based on artificial neural network model of landslide susceptibility (case study: Kojour watershed)

Document Type : Research Paper

Authors

R. Naghdi ¹

A. Abbasian ²

I. Ghajar ¹

¹ Assoc. Prof., Department of Forestry, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, I.R. Iran

² Department of forestry, Faculty of natural resources, University of Guilan, Sowmeh Sara, Iran

10.22059/jfwp.2017.212553.767

Abstract

This research aimed at modeling the landslide susceptibility using the Artificial Neural Networks (ANN) in Kojour watershed and then planning the forest road based on the resulted map in Aghoozchal and Moor districts in the northern Iran. After recording the coordinates of 95 occurred landslides, six factors of terrain slope, aspect, curvature, distance to river, distance to fault, and geology formation were assumed as the effective factors in landslide occurrence. The digital layers of input variables were prepared in Geographic Information System. After data extraction, various backpropagation multi-layer perceptron ANNs with various setting parameters were developed and their performances were evaluated. Results showed that the best root mean square error (RMSE) and determination coefficient (R²) as model performance criteria for the most robust model were 0.1945 and 0.8676, respectively, in which 2 and 8 neurons have been obtained in the first and second hidden layers. Among the proposed variants, the variant No. 3 was selected as the most appropriate one with the least passing from very susceptible landslide classes and it was then implemented in the field.

Keywords

Backpropagation

Forest road

Landslide

Neuron

Stability