Scholarly record

LANDSLIDE SUSCEPTIBILITY MAPPING USING FREQUENCY RATIO, LOGISTIC REGRESSION, ARTIFICIAL NEURAL NETWORKS AND THEIR COMPARISON: A CASE STUDY FROM KAT LANDSLIDES (TOKAT-TURKEY)

YILMAZ

Abstract

This case study presented herein compares the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat county (Tokat-Turkey). Digital Elevation Model (DEM) was first constructed using a GIS software. Parameter maps affecting the slope stability such as; geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were then produced from DEM of the study area. In the last stage of the analyses, landslide susceptibility maps were produced using the frequency ratio, logistic regression and neural networks, and they were then compared by means of their validations. As a result of this study, higher accuracies of susceptibility maps for all three models were obtained. However respective coefficient of correlations 84.6\%, 86.2\%, 87.7\% for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of the landslide susceptibility when a sufficient number of data was obtained. Because input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require a conversion of data used in analyses into ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

Publication details

Back to publication list